Final Year IEEE Project 2013-2014 - Parallel and Distributed Systems Project Title and Abstract

Elysium Technologies Private Limited
Singapore | Madurai | Chennai | Trichy | Coimbatore | Cochin | Ramnad |
Pondicherry | Trivandrum | Salem | Erode | Tirunelveli
http://www.elysiumtechnologies.com, info@elysiumtechnologies.com
13 Years of Experience
Automated Services
24/7 Help Desk Support
Experience & Expertise Developers
Advanced Technologies & Tools
Legitimate Member of all Journals
Having 1,50,000 Successive records in
all Languages
More than 12 Branches in Tamilnadu,
Kerala & Karnataka.
Ticketing & Appointment Systems.
Individual Care for every Student.
Around 250 Developers & 20
Researchers

227-230 Church Road, Anna Nagar, Madurai – 625020.
0452-4390702, 4392702, + 91-9944793398.
info@elysiumtechnologies.com, elysiumtechnologies@gmail.com
S.P.Towers, No.81 Valluvar Kottam High Road, Nungambakkam,
Chennai - 600034. 044-42072702, +91-9600354638,
chennai@elysiumtechnologies.com
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0431-4002234, + 91-9790464324.
trichy@elysiumtechnologies.com
577/4, DB Road, RS Puram, Opp to KFC, Coimbatore – 641002
0422- 4377758, +91-9677751577.
coimbatore@elysiumtechnologies.com

Plot No: 4, C Colony, P&T Extension, Perumal puram, Tirunelveli-
627007. 0462-2532104, +919677733255,
tirunelveli@elysiumtechnologies.com
1st Floor, A.R.IT Park, Rasi Color Scan Building, Ramanathapuram
- 623501. 04567-223225,
+919677704922.ramnad@elysiumtechnologies.com
74, 2nd floor, K.V.K Complex,Upstairs Krishna Sweets, Mettur
Road, Opp. Bus stand, Erode-638 011. 0424-4030055, +91-
9677748477 erode@elysiumtechnologies.com
No: 88, First Floor, S.V.Patel Salai, Pondicherry – 605 001. 0413–
4200640 +91-9677704822
pondy@elysiumtechnologies.com
TNHB A-Block, D.no.10, Opp: Hotel Ganesh Near Busstand. Salem
– 636007, 0427-4042220, +91-9894444716.
salem@elysiumtechnologies.com

ETPL
PDS-001
Adaptive Network Coding for Broadband Wireless Access Networks
Abstract: Broadband wireless access (BWA) networks, such as LTE and WiMAX, are inherently lossy
due to wireless medium unreliability. Although the Hybrid Automatic Repeat reQuest (HARQ) error-
control method recovers from packet loss, it has low transmission efficiency and is unsuitable for delay-
sensitive applications. Alternatively, network coding techniques improve the throughput of wireless
networks, but incur significant overhead and ignore network constraints such as Medium Access Control
(MAC) layer transmission opportunities and physical (PHY) layer channel conditions. The present study
provides analysis of Random Network Coding (RNC) and Systematic Network Coding (SNC) decoding
probabilities. Based on the analytical results, SNC is selected for developing an adaptive network coding
scheme designated as Frame-by-frame Adaptive Systematic Network Coding (FASNC). According to
network constraints per frame, FASNC dynamically utilizes either Modified Systematic Network Coding
(M-SNC) or Mixed Generation Coding (MGC). An analytical model is developed for evaluating the mean
decoding delay and mean goodput of the proposed FASNC scheme. The results derived using this model
agree with those obtained from computer simulations. Simulations show that FASNC results in both
lower decoding delay and reduced buffer requirements compared to MRNC and N-in-1 ReTX, while also
yielding higher goodput than HARQ, MRNC, and N-in-1 ReTX.
ETPL
PDS-002
Covering Points of Interest with Mobile Sensors
Abstract: The coverage of Points of Interest (PoI) is a classical requirement in mobile wireless sensor
applications. Optimizing the sensors self-deployment over a PoI while maintaining the connectivity
between the sensors and the base station is thus a fundamental issue. This paper addresses the problem of
autonomous deployment of mobile sensors that need to cover a predefined PoI with a connectivity
constraint. In our algorithm, each sensor moves toward a PoI but has also to maintain the connectivity
with a subset of its neighboring sensors that are part of the Relative Neighborhood Graph (RNG). The
Relative Neighborhood Graph reduction is chosen so that global connectivity can be provided locally.
Our deployment scheme minimizes the number of sensors used for connectivity thus increasing the
number of monitoring sensors. Analytical results, simulation results and practical implementation are
provided to show the efficiency of our algorithm.
ETPL
PDS-003
Detection and Localization of Multiple Spoofing Attackers in Wireless Networks
Abstract: Wireless spoofing attacks are easy to launch and can significantly impact the performance of
networks. Although the identity of a node can be verified through cryptographic authentication,
conventional security approaches are not always desirable because of their overhead requirements. In this
paper, we propose to use spatial information, a physical property associated with each node, hard to
falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the
number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing
multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited
from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the
number of attackers as a multiclass detection problem. Cluster-based mechanisms are developed to
determine the number of attackers. When the training data are available, we explore using the Support
Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers.

In addition, we developed an integrated detection and localization system that can localize the positions of
multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi)
network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show
that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the
number of attackers. Our localization results using a representative set of algorithms provide strong
evidence of high accuracy of localizing multiple adversaries.
ETPL
PDS-004
Efficient Eager Management of Conflicts for Scalable Hardware Transactional
Memory
Abstract: The efficient management of conflicts among concurrent transactions constitutes a key aspect
that hardware transactional memory (HTM) systems must achieve. Scalable HTM proposals so far inherit
the cache-based style of conflict detection typically found in bus-based systems, largely unaware of the
interactions between transactions and directory coherence. In this paper, we demonstrate that the
traditional approach of detecting conflicts at the private cache levels is inefficient when used in the
context of a directory protocol. We find that the use of the directory as a mere router of coherence
requests restricts the throughput of conflict detection, and show how it becomes a bottleneck under high
contention. This paper proposes a scheme for conflict detection that decouples conflict detection from
cache coherence in order to overcome pathological situations that degrade the performance of an eager
HTM system. Our scheme places bookkeeping metadata at the directory, introducing it as a separate
hardware module that leaves the coherence protocol unmodified. In comparison to a state-of-the-art eager
HTM system, our design handles contention more efficiently, minimizes the performance degradation of
false positives for signatures of similar hardware cost, and reduces the network traffic generated.
ETPL
PDS-005
High Performance Resource Allocation Strategies for Computational Economies
Abstract: Utility computing models have long been the focus of academic research, and with the recent
success of commercial cloud providers, computation and storage is finally being realized as the fifth
utility. Computational economies are often proposed as an efficient means of resource allocation,
however adoption has been limited due to a lack of performance and high overheads. In this paper, we
address the performance limitations of existing economic allocation models by defining strategies to
reduce the failure and reallocation rate, increase occupancy and thereby increase the obtainable utilization
of the system. The high-performance resource utilization strategies presented can be used by market
participants without requiring dramatic changes to the allocation protocol. The strategies considered
include overbooking, advanced reservation, just-in-time bidding, and using substitute providers for
service delivery. The proposed strategies have been implemented in a distributed metascheduler and
evaluated with respect to Grid and cloud deployments. Several diverse synthetic workloads have been
used to quantity both the performance benefits and economic implications of these strategies.
ETPL
PDS-006
Mapping a Jacobi Iterative Solver onto a High-Performance Heterogeneous Computer
Abstract: High-performance heterogeneous computers that employ field programmable gate arrays
(FPGAs) as computational elements are known as high-performance reconfigurable computers (HPRCs).
For floating-point applications, these FPGA-based processors must satisfy a variety of heuristics and rules
of thumb to achieve a speedup compared with their software counterparts. By way of a simple sparse
matrix Jacobi iterative solver, this paper illustrates some of the issues associated with mapping floating-

point kernels onto HPRCs. The Jacobi method was chosen based on heuristics developed from earlier
research. Furthermore, Jacobi is relatively easy to understand, yet is complex enough to illustrate the
mapping issues. This paper is not trying to demonstrate the speedup of a particular application nor is it
suggesting that Jacobi is the best way to solve equations. The results demonstrate a nearly threefold wall
clock runtime speedup when compared with a software implementation. A formal analysis shows that
these results are reasonable. The purpose of this paper is to illuminate the challenging floating-point
mapping process while simultaneously showing that such mappings can result in significant speedups.
The ideas revealed by research such as this have already been and should continue to be used to facilitate
a more automated mapping process.
ETPL
PDS-007
MIN-MAX: A Counter-Based Algorithm for Regular Expression Matching
Abstract: We propose an NFA-based algorithm called MIN-MAX to support matching of regular
expressions (regexp) composed of Character Classes with Constraint Repetitions (CCR). MIN-MAX is
well suited for massive parallel processing architectures, such as FPGAs, yet it is effective on any other
computing platform. In MIN-MAX, each active CCR engine (to implement one CCR term) evaluates
input characters, updates (MIN, MAX) counters, and asserts control signals, and all the CCR engines
implemented in the FPGA run simultaneously. Unlike traditional designs, (MIN, MAX) counters contain
dynamically updated lower and upper bounds of possible matching counts, instead of actual matching
counts, so that feasible matching lengths are compactly enclosed in the counter value. The counter-based
design can support constraint repetitions of n using O({rm log} n) memory bits rather than that of O(n) in
existing solutions. MIN-MAX can resolve character class ambiguity between adjacent CCR terms and
support overlapped matching when matching collisions are absent. We developed a set of heuristic rules
to assess the absence of collision for CCR-based regexps, and tested them on Snort and SpamAssassin
rule sets. The results show that the vast majority of rules are immune from collisions, so that MIN-MAX
can cost effectively support overlapped matching. As a bonus, the new architecture also supports fast
reconfiguration via ordinary memory writes rather than resynthesis of the entire design, which is critical
for time-sensitive regexp deployment scenarios.
ETPL
PDS-008
Network Traffic Classification Using Correlation Information
Abstract: Traffic classification has wide applications in network management, from security monitoring
to quality of service measurements. Recent research tends to apply machine learning techniques to flow
statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited
superior classification performance. It also has several important advantages, such as no requirements of
training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number
of classes. However, the performance of NN classifier can be severely affected if the size of training data
is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can
improve the classification performance effectively by incorporating correlated information into the
classification process. We analyze the new classification approach and its performance benefit from both
theoretical and empirical perspectives. A large number of experiments are carried out on two real-world
traffic data sets to validate the proposed approach. The results show the traffic classification performance
can be improved significantly even under the extreme difficult circumstance of very few training samples.

ETPL
PDS-009
Online Real-Time Task Scheduling in Heterogeneous Multicore System-on-a-Chip
Abstract: Online task scheduling in heterogeneous multicore system-on-a-chip is a challenging problem
due to precedence constraints and nonpreemptive task execution in the synergistic processor core. This
study first proposes an online heterogeneous dual-core scheduling framework for dynamic workloads
with real-time constraints. The general purpose processor core and the synergistic processor core are
dedicated to separate schedulers with different scheduling policies, and precedence constraints among
tasks are dealt with through interaction between the two schedulers. This framework is also configurable
for low priority inversion and high system utilization. We then extend this framework to heterogeneous
multicore systems with well-known dispatcher schemas. This paper presents a real case study to show the
practicability of the proposed methodology, and presents a series of extensive simulations to obtain
comparison studies using different workloads and scheduling algorithms.
ETPL
PDS-010
Scalable and Secure Sharing of Personal Health Records in Cloud Computing Using
Attribute-Based Encryption,
Abstract: Personal health record (PHR) is an emerging patient-centric model of health information
exchange, which is often outsourced to be stored at a third party, such as cloud providers. However, there
have been wide privacy concerns as personal health information could be exposed to those third party
servers and to unauthorized parties. To assure the patients' control over access to their own PHRs, it is a
promising method to encrypt the PHRs before outsourcing. Yet, issues such as risks of privacy exposure,
scalability in key management, flexible access, and efficient user revocation, have remained the most
important challenges toward achieving fine-grained, cryptographically enforced data access control. In
this paper, we propose a novel patient-centric framework and a suite of mechanisms for data access
control to PHRs stored in semitrusted servers. To achieve fine-grained and scalable data access control for
PHRs, we leverage attribute-based encryption (ABE) techniques to encrypt each patient's PHR file.
Different from previous works in secure data outsourcing, we focus on the multiple data owner scenario,
and divide the users in the PHR system into multiple security domains that greatly reduces the key
management complexity for owners and users. A high degree of patient privacy is guaranteed
simultaneously by exploiting multiauthority ABE. Our scheme also enables dynamic modification of
access policies or file attributes, supports efficient on-demand user/attribute revocation and break-glass
access under emergency scenarios. Extensive analytical and experimental results are presented which
show the security, scalability, and efficiency of our proposed scheme.
ETPL
PDS-011
Strategies for Energy-Efficient Resource Management of Hybrid Programming Models
Abstract: Many scientific applications are programmed using hybrid programming models that use both
message passing and shared memory, due to the increasing prevalence of large-scale systems with
multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using
software-controlled execution schemes that consider both the programming model and the power-aware
execution capabilities of the system. However, such approaches have focused on identifying optimal
resource utilization for one programming model, either shared memory or message passing, in isolation.
The potential solution space, thus the challenge, increases substantially when optimizing hybrid models
since the possible resource configurations increase exponentially. Nonetheless, with the accelerating
adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid

parallel applications on large-scale systems. In this work, we present new software-controlled execution
schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and
frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present
predictive models and novel algorithms based on statistical analysis that anticipate application power and
time requirements under different concurrency and frequency configurations. We apply our models and
methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we
achieve substantial energy savings (8.74 percent on average and up to 13.8 percent) with some
performance gain (up to 7.5 percent) or negligible performance loss.
ETPL
PDS-012
Supporting HPC Analytics Applications with Access Patterns Using Data
Restructuring and Data-Centric Scheduling Techniques in MapReduce
Abstract: Current High Performance Computing (HPC) applications have seen an explosive growth in the
size of data in recent years. Many application scientists have initiated efforts to integrate data-intensive
computing into computational-intensive HPC facilities, particularly for data analytics. We have observed
several scientific applications which must migrate their data from an HPC storage system to a data-
intensive one for analytics. There is a gap between the data semantics of HPC storage and data-intensive
system, hence, once migrated, the data must be further refined and reorganized. This reorganization must
be performed before existing data-intensive tools such as MapReduce can be used to analyze data. This
reorganization requires at least two complete scans through the data set and then at least one MapReduce
program to prepare the data before analyzing it. Running multiple MapReduce phases causes significant
overhead for the application, in the form of excessive I/O operations. That is for every MapReduce phase,
a distributed read and write operation on the file system must be performed. Our contribution is to
develop a MapReduce-based framework for HPC analytics to eliminate the multiple scans and also reduce
the number of data preprocessing MapReduce programs. We also implement a data-centric scheduler to
further improve the performance of HPC analytics MapReduce programs by maintaining the data locality.
We have added additional expressiveness to the MapReduce language to allow application scientists to
specify the logical semantics of their data such that 1) the data can be analyzed without running multiple
data preprocessing MapReduce programs, and 2) the data can be simultaneously reorganized as it is
migrated to the data-intensive file system. Using our augmented Map-Reduce system, MapReduce with
Access Patterns (MRAP), we have demonstrated up to 33 percent throughput improvement in one real
application, and up to 70 percent in an I/O kernel of another appl- cation. Our results for scheduling show
up to 49 percent improvement for an I/O kernel of a prevalent HPC analysis application.
ETPL
PDS-013
Thermal and Energy Management of High-Performance Multicores: Distributed and
Self-Calibrating Model-Predictive Controller
Abstract: As result of technology scaling, single-chip multicore power density increases and its spatial
and temporal workload variation leads to temperature hot-spots, which may cause nonuniform ageing and
accelerated chip failure. These critical issues can be tackled by closed-loop thermal and reliability
management policies. Model predictive controllers (MPC) outperform classic feedback controllers since
they are capable of minimizing performance loss while enforcing safe working temperature.
Unfortunately, MPC controllers rely on a priori knowledge of thermal models and their complexity
exponentially grows with the number of controlled cores. In this paper, we present a scalable, fully
distributed, energy-aware thermal management solution for single-chip multicore platforms. The model-
predictive controller complexity is drastically reduced by splitting it in a set of simpler interacting
controllers, each one allocated to a core in the system. Locally, each node selects the optimal frequency to

meet temperature constraints while minimizing the performance penalty and system energy. Comparable
performance with state-of-the-art MPC controllers is achieved by letting controllers exchange a limited
amount of information at runtime on a neighborhood basis. In addition, we address model uncertainty by
supporting learning of the thermal model with a novel distributed self-calibration approach that matches
well the controller architecture.
ETPL
PDS-014
Topology Abstraction Service for IP-VPNs
Abstract: VPN service providers (VSP) and IP-VPN customers have traditionally maintained service
demarcation boundaries between their routing and signaling entities. This has resulted in the VPNs
viewing the VSP network as an opaque entity and therefore limiting any meaningful interaction between
the VSP and the VPNs. The purpose of this research is to address this issue by enabling a VSP to share its
core topology information with the VPNs through a novel topology abstraction (TA) service which is
both practical and scalable in the context of managed IP-VPNs. TA service provides tunable visibility of
state of the VSP's network leading to better VPN performance. A key challenge of the TA service is to
generate TA with relevant network resource information for each VPN in an accurate and fair manner.
We develop three decentralized schemes for generating TAs with different performance characteristics.
These decentralized schemes achieve improved call performance, fair resource sharing for VPNs, and
higher network utilization for the VSP. We validate the idea of the VPN TA service and study the
performance of the proposed techniques using various simulation scenarios over several topologies.
ETPL
PDS-015
A Secure Payment Scheme with Low Communication and Processing Overhead for
Multihop Wireless Networks
Abstract: We propose RACE, a report-based payment scheme for multihop wireless networks to stimulate
node cooperation, regulate packet transmission, and enforce fairness. The nodes submit lightweight
payment reports (instead of receipts) to the accounting center (AC) and temporarily store undeniable
security tokens called Evidences. The reports contain the alleged charges and rewards without security
proofs, e.g., signatures. The AC can verify the payment by investigating the consistency of the reports,
and clear the payment of the fair reports with almost no processing overhead or cryptographic operations.
For cheating reports, the Evidences are requested to identify and evict the cheating nodes that submit
incorrect reports. Instead of requesting the Evidences from all the nodes participating in the cheating
reports, RACE can identify the cheating nodes with requesting few Evidences. Moreover, Evidence
aggregation technique is used to reduce the Evidences' storage area. Our analytical and simulation results
demonstrate that RACE requires much less communication and processing overhead than the existing
receipt-based schemes with acceptable payment clearance delay and storage area. This is essential for the
effective implementation of a payment scheme because it uses micropayment and the overhead cost
should be much less than the payment value. Moreover, RACE can secure the payment and precisely
identify the cheating nodes without false accusations.
ETPL
PDS-016
Analysis of Distance-Based Location Management in Wireless Communication
Networks
Abstract: The performance of dynamic distance-based location management schemes (DBLMS) in

wireless communication networks is analyzed. A Markov chain is developed as a mobility model to
describe the movement of a mobile terminal in 2D cellular structures. The paging area residence time is
characterized for arbitrary cell residence time by using the Markov chain. The expected number of paging
area boundary crossings and the cost of the distance-based location update method are analyzed by using
the classical renewal theory for two different call handling models. For the call plus location update
model, two cases are considered. In the first case, the intercall time has an arbitrary distribution and the
cell residence time has an exponential distribution. In the second case, the intercall time has a hyper-
Erlang distribution and the cell residence time has an arbitrary distribution. For the call without location
update model, both intercall time and cell residence time can have arbitrary distributions. Our analysis
makes it possible to find the optimal distance threshold that minimizes the total cost of location
management in a DBLMS.
ETPL
PDS-017
Cluster-Based Certificate Revocation with Vindication Capability for Mobile Ad Hoc
Networks,
Abstract: Mobile ad hoc networks (MANETs) have attracted much attention due to their mobility and
ease of deployment. However, the wireless and dynamic natures render them more vulnerable to various
types of security attacks than the wired networks. The major challenge is to guarantee secure network
services. To meet this challenge, certificate revocation is an important integral component to secure
network communications. In this paper, we focus on the issue of certificate revocation to isolate attackers
from further participating in network activities. For quick and accurate certificate revocation, we propose
the Cluster-based Certificate Revocation with Vindication Capability (CCRVC) scheme. In particular, to
improve the reliability of the scheme, we recover the warned nodes to take part in the certificate
revocation process; to enhance the accuracy, we propose the threshold-based mechanism to assess and
vindicate warned nodes as legitimate nodes or not, before recovering them. The performances of our
scheme are evaluated by both numerical and simulation analysis. Extensive results demonstrate that the
proposed certificate revocation scheme is effective and efficient to guarantee secure communications in
mobile ad hoc networks.
ETPL
PDS-018
Coloring-Based Inter-WBAN Scheduling for Mobile Wireless Body Area Networks
Abstract: In this study, random incomplete coloring (RIC) with low time-complexity and high spatial
reuse is proposed to overcome in-between wireless-body-area-networks (WBAN) interference, which can
cause serious throughput degradation and energy waste. Interference-avoidance scheduling of wireless
networks can be modeled as a problem of graph coloring. For instance, high spatial-reuse scheduling for a
dense sensor network is mapped to high spatial-reuse coloring; fast convergence scheduling for a mobile
ad hoc network (MANET) is mapped to low time-complexity coloring. However, for a dense and mobile
WBAN, inter-WBAN scheduling (IWS) should simultaneously satisfy both of the following
requirements: 1) high spatial-reuse and 2) fast convergence, which are tradeoffs in conventional coloring.
By relaxing the coloring rule, the proposed distributed coloring algorithm RIC avoids this tradeoff and
satisfies both requirements. Simulation results verify that the proposed coloring algorithm effectively
overcomes inter-WBAN interference and invariably supports higher system throughput in various mobile
WBAN scenarios compared to conventional colorings.
ETPL
PDS-019
Cross-Layer Design of Congestion Control and Power Control in Fast-Fading Wireless
Networks

Abstract: We study the cross-layer design of congestion control and power allocation with outage
constraint in an interference-limited multihop wireless networks. Using a complete-convexification
method, we first propose a message-passing distributed algorithm that can attain the global optimal source
rate and link power allocation. Despite the attractiveness of its optimality, this algorithm requires larger
message size than that of the conventional scheme, which increases network overheads. Using the bounds
on outage probability, we map the outage constraint to an SIR constraint and continue developing a
practical near-optimal distributed algorithm requiring only local SIR measurement at link receivers to
limit the size of the message. Due to the complicated complete-convexification method, however the
congestion control of both algorithms no longer preserves the existing TCP stack. To take into account
the TCP stack preserving property, we propose the third algorithm using a successive convex
approximation method to iteratively transform the original nonconvex problem into approximated convex
problems, then the global optimal solution can converge distributively with message-passing. Thanks to
the tightness of the bounds and successive approximations, numerical results show that the gap between
three algorithms is almost indistinguishable. Despite the same type of the complete-convexification
method, the numerical comparison shows that the second near-optimal scheme has a faster convergence
rate than that of the first optimal one, which make the near-optimal scheme more favorable and applicable
in practice. Meanwhile, the third optimal scheme also has a faster convergence rate than that of a previous
work using logarithm successive approximation method.
ETPL
PDS-020
Distributed Data Replenishment
Abstract: We propose a distributed data replenishment mechanism for some distributed peer-to-peer-
based storage systems that automates the process of maintaining a sufficient level of data redundancy to
ensure the availability of data in presence of peer departures and failures. The dynamics of peers entering
and leaving the network are modeled as a stochastic process. A novel analytical time-backward technique
is proposed to bound the expected time for a piece of data to remain in P2P systems. Both theoretical and
simulation results are in agreement, indicating that the data replenishment via random linear network
coding (RLNC) outperforms other popular strategies. Specifically, we show that the expected time for a
piece of data to remain in a P2P system, the longer the better, is exponential in the number of peers used
to store the data for the RLNC-based strategy, while they are quadratic for other strategies.
ETPL
PDS-021
Distributed k-Core Decomposition
Abstract: Several novel metrics have been proposed in recent literature in order to study the relative
importance of nodes in complex networks. Among those, k-coreness has found a number of applications
in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and
design. This paper proposes new distributed algorithms for the computation of the k-coreness of a
network, a process also known as k-core decomposition. This technique 1) allows the decomposition,
over a set of connected machines, of very large graphs, when size does not allow storing and processing
them on a single host, and 2) enables the runtime computation of k-cores in “live” distributed systems.
Lower bounds on the algorithms complexity are given, and an exhaustive experimental analysis on real-
world data sets is provided.

ETPL
PDS-022
Dynamic Coverage of Mobile Sensor Networks
Abstract: We study the dynamic aspects of the coverage of a mobile sensor network resulting from
continuous movement of sensors. As sensors move around, initially uncovered locations may be covered
at a later time, and intruders that might never be detected in a stationary sensor network can now be
detected by moving sensors. However, this improvement in coverage is achieved at the cost that a
location is covered only part of the time, alternating between covered and not covered. We characterize
area coverage at specific time instants and during time intervals, as well as the time durations that a
location is covered and uncovered. We further consider the time it takes to detect a randomly located
intruder and prove that the detection time is exponentially distributed with parameter 2λrv̅ s where λ
represents the sensor density , r represents the sensor 's sensing range , and v̅ s denotes the average sensor
speed. For mobile intruders, we take a game theoretic approach and derive optimal mobility strategies for
both sensors and intruders. We prove that the optimal sensor strategy is to choose their directions
uniformly at random between (0, 2π). The optimal intruder strategy is to remain stationary. This solution
represents a mixed strategy which is a Nash equilibrium of the zero-sum game between mobile sensors
and intruders.
ETPL
PDS-023
Exploiting Ubiquitous Data Collection for Mobile Users in Wireless Sensor Networks
Abstract: We study the ubiquitous data collection for mobile users in wireless sensor networks. People
with handheld devices can easily interact with the network and collect data. We propose a novel approach
for mobile users to collect the network-wide data. The routing structure of data collection is additively
updated with the movement of the mobile user. With this approach, we only perform a limited
modification to update the routing structure while the routing performance is bounded and controlled
compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows
that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing
performance, and provides continuous data delivery during the user movement. We implement the
proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We
further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied
network settings.
ETPL
PDS-024
Fast Channel Zapping with Destination-Oriented Multicast for IP Video Delivery
Abstract: Channel zapping time is a critical quality of experience (QoE) metric for IP-based video
delivery systems such as IPTV. An interesting zapping acceleration scheme based on time-shifted
subchannels (TSS) was recently proposed, which can ensure a zapping delay bound as well as maintain
the picture quality during zapping. However, the behaviors of the TSS-based scheme have not been fully
studied yet. Furthermore, the existing TSS-based implementation adopts the traditional IP multicast,
which is not scalable for a large-scale distributed system. Corresponding to such issues, this paper makes
contributions in two aspects. First, we resort to theoretical analysis to understand the fundamental
properties of the TSS-based service model. We show that there exists an optimal subchannel data rate
which minimizes the redundant traffic transmitted over subchannels. Moreover, we reveal a start-up
effect, where the existing operation pattern in the TSS-based model could violate the zapping delay
bound. With a solution proposed to resolve the start-up effect, we rigorously prove that a zapping delay

bound equal to the subchannel time shift is guaranteed by the updated TSS-based model. Second, we
propose a destination-oriented-multicast (DOM) assisted zapping acceleration (DAZA) scheme for a
scalable TSS-based implementation, where a subscriber can seamlessly migrate from a subchannel to the
main channel after zapping without any control message exchange over the network. Moreover, the
subchannel selection in DAZA is independent of the zapping request signaling delay, resulting in
improved robustness and reduced messaging overhead in a distributed environment. We implement
DAZA in ns-2 and multicast an MPEG-4 video stream over a practical network topology. Extensive
simulation results are presented to demonstrate the validity of our analysis and DAZA scheme.
ETPL
PDS-025
Gaussian versus Uniform Distribution for Intrusion Detection in Wireless Sensor
Networks
Abstract: In a Wireless Sensor Network (WSN), intrusion detection is of significant importance in many
applications in detecting malicious or unexpected intruder(s). The intruder can be an enemy in a
battlefield, or a malicious moving object in the area of interest. With uniform sensor deployment, the
detection probability is the same for any point in a WSN. However, some applications may require
different degrees of detection probability at different locations. For example, an intrusion detection
application may need improved detection probability around important entities. Gaussian-distributed
WSNs can provide differentiated detection capabilities at different locations but related work is limited.
This paper analyzes the problem of intrusion detection in a Gaussian-distributed WSN by characterizing
the detection probability with respect to the application requirements and the network parameters under
both single-sensing detection and multiple-sensing detection scenarios. Effects of different network
parameters on the detection probability are examined in detail. Furthermore, performance of Gaussian-
distributed WSNs is compared with uniformly distributed WSNs. This work allows us to analytically
formulate detection probability in a random WSN and provides guidelines in selecting an appropriate
deployment strategy and determining critical network parameters.
ETPL
PDS-026
IDM: An Indirect Dissemination Mechanism for Spatial Voice Interaction in
Networked Virtual Environments
Abstract: One type of Peer-to-Peer (P2P) live streaming has not yet been significantly investigated,
namely topologies that provide many-to-many, interactive connectivity. Exemplar applications of such
P2P systems include spatial audio services for networked virtual environments (NVEs) and distributed
online games. Numerous challenging problems have to be overcome-among them providing low delay,
resilience to churn, effective load balancing, and rapid convergence-in such dynamic environments. We
propose a novel P2P overlay dissemination mechanism, termed IDM, that can satisfy such demanding
real-time requirements. Our target application is to provide spatialized voice support in multiplayer
NVEs, where each bandwidth constrained peer potentially communicates with all other peers within its
area-of-interest (AoI). With IDM each peer maintains a set of partners, termed helpers, which may act as
stream forwarders. We prove analytically that the system reachability is maximized when the loads of
helpers are balanced proportionally to their network capacities. We then propose a game-theoretic
algorithm that balances the loads of the peers in a fully distributed manner. Of practical importance in
dynamic systems, we prove that our algorithm converges to an approximately balanced state from any
prior state in rapid O(log log n) time, where n is the number of users. We further evaluate our technique
with simulations and show that it can achieve near optimal system reachability and satisfy the tight
latency constraints of interactive audio under conditions of churn, avatar mobility, and heterogeneous user
access network bandwidth.

ETPL
PDS-027
In-Network Estimation with Delay Constraints in Wireless Sensor Networks
Abstract: The use of wireless sensor networks (WSNs) for closing the loops between the cyberspace and
the physical processes is more attractive and promising for future control systems. For some real-time
control applications, controllers need to accurately estimate the process state within rigid delay
constraints. In this paper, we propose a novel in-network estimation approach for state estimation with
delay constraints in multihop WSNs. For accurately estimating a process state as well as satisfying rigid
delay constraints, we address the problem through jointly designing in-network estimation operations and
an aggregation scheduling algorithm. Our in-network estimation operation performed at relays not only
optimally fuses the estimates obtained from the different sensors but also predicts the upper stream
sensors' estimates which cannot be aggregated to the sink before deadlines. Our estimate aggregation
scheduling algorithm, which is interference free, is able to aggregate as much estimate information as
possible from the network to the sink within delay constraints. We proved the unbiasedness of in-network
estimation, and theoretically analyzed the optimality of our approach. Our simulation results corroborate
our theoretical results and show that our in-network estimation approach can obtain significant estimation
accuracy gain under different network settings.
ETPL
PDS-028
IP-Geolocation Mapping for Moderately Connected Internet Regions
Abstract: Most IP-geolocation mapping schemes [14], [16], [17], [18] take delay-measurement approach,
based on the assumption of a strong correlation between networking delay and geographical distance
between the targeted client and the landmarks. In this paper, however, we investigate a large region of
moderately connected Internet and find the delay-distance correlation is weak. But we discover a more
probable rule - with high probability the shortest delay comes from the closest distance. Based on this
closest-shortest rule, we develop a simple and novel IP-geolocation mapping scheme for moderately
connected Internet regions, called GeoGet. In GeoGet, we take a large number of webservers as passive
landmarks and map a targeted client to the geolocation of the landmark that has the shortest delay. We
further use JavaScript at targeted clients to generate HTTP/Get probing for delay measurement. To
control the measurement cost, we adopt a multistep probing method to refine the geolocation of a targeted
client, finally to city level. The evaluation results show that when probing about 100 landmarks, GeoGet
correctly maps 35.4 percent clients to city level, which outperforms current schemes such as GeoLim [16]
and GeoPing [14] by 270 and 239 percent, respectively, and the median error distance in GeoGet is
around 120 km, outperforming GeoLim and GeoPing by 37 and 70 percent, respectively.
ETPL
PDS-029
Microarchitecture of a Coarse-Grain Out-of-Order Superscalar Processor
Abstract: We explore the design, implementation, and evaluation of a coarse-grain superscalar processor
in the context of the microarchitecture of the Control Processor (CP) of the Multilevel Computing
Architecture (MLCA), a novel architecture targeted for multimedia multicore systems. The MLCA
augments a traditional multicore architecture (called the lower level) with a CP (called the top-level),
which automatically extracts parallelism among coarse-grain units of computation (tasks), synchronizes
these tasks and schedules them for execution on processors. It does so in a fashion similar to how
instruction-level parallelism is extracted by superscalar processors, i.e., using register renaming, Out-of-
Order Execution (OoOE) and scheduling. The coarse-grain nature of tasks imposes challenging

constraints on the direct use of these techniques, but also offers opportunities for simpler designs. We
analyze the impact of these constraints and opportunities and present novel microarchitectural
mechanisms for coarse-grain superscalar execution, including register renaming, task queue, dynamic out-
of-order scheduling and task-issue. We design an MLCA system around our CP microarchitecture and
implement it on an FPGA. We evaluate the system using multimedia applications and show good
scalability for eight processors, limited by the memory bandwidth of the FPGA platform. Furthermore,
we show that the CP introduces little overhead in terms of resource usage. Finally, we show scalability
beyond eight processors using cycle-accurate RTL-level simulation with an idealized memory subsystem.
We demonstrate that the CP poses no performance bottlenecks and is scalable up to 32 processors.
ETPL
PDS-030
Mobi-Sync: Efficient Time Synchronization for Mobile Underwater Sensor Networks
Abstract: Time synchronization is an important requirement for many services provided by distributed
networks. A lot of time synchronization protocols have been proposed for terrestrial Wireless Sensor
Networks (WSNs). However, none of them can be directly applied to Underwater Sensor Networks
(UWSNs). A synchronization algorithm for UWSNs must consider additional factors such as long
propagation delays from the use of acoustic communication and sensor node mobility. These unique
challenges make the accuracy of synchronization procedures for UWSNs even more critical. Time
synchronization solutions specifically designed for UWSNs are needed to satisfy these new requirements.
This paper proposes Mobi-Sync, a novel time synchronization scheme for mobile underwater sensor
networks. Mobi-Sync distinguishes itself from previous approaches for terrestrial WSN by considering
spatial correlation among the mobility patterns of neighboring UWSNs nodes. This enables Mobi-Sync to
accurately estimate the long dynamic propagation delays. Simulation results show that Mobi-Sync
outperforms existing schemes in both accuracy and energy efficiency.
ETPL
PDS-031
Autogeneration and Autotuning of 3D Stencil Codes on Homogeneous and
Heterogeneous GPU Clusters
Abstract: This paper develops and evaluates search and optimization techniques for autotuning 3D stencil
(nearest neighbor) computations on GPUs. Observations indicate that parameter tuning is necessary for
heterogeneous GPUs to achieve optimal performance with respect to a search space. Our proposed
framework takes a most concise specification of stencil behavior from the user as a single formula,
autogenerates tunable code from it, systematically searches for the best configuration and generates the
code with optimal parameter configurations for different GPUs. This autotuning approach guarantees
adaptive performance for different generations of GPUs while greatly enhancing programmer
productivity. Experimental results show that the delivered floating point performance is very close to
previous handcrafted work and outperforms other autotuned stencil codes by a large margin. Furthermore,
heterogeneous GPU clusters are shown to exhibit the highest performance for dissimilar tuning
parameters leveraging proportional partitioning relative to single-GPU performance.
ETPL
PDS-032
An Iterative Divide-and-Merge-Based Approach for Solving Large-Scale Least Squares
Problems
Abstract: Singular value decomposition (SVD) is a popular decomposition method for solving least
squares estimation (LSE) problems. However, for large data sets, applying SVD directly on the
coefficient matrix is very time consuming and memory demanding in obtaining least squares solutions. In

this paper, we propose an iterative divide-and-merge-based estimator for solving large-scale LSE
problems. Iteratively, the LSE problem to be solved is processed and transformed to equivalent but
smaller LSE problems. In each iteration, the input matrices are subdivided into a set of small submatrices.
The submatrices are decomposed by SVD, respectively, and the results are merged, and the resulting
matrices become the input of the next iteration. The process is iterated until the resulting matrices are
small enough which can then be solved directly and efficiently by SVD. The number of iterations
required is determined dynamically according to the size of the input data set. As a result, the
requirements in time and space for finding least squares solutions are greatly improved. Furthermore, the
decomposition and merging of the submatrices in each iteration can be independently done in parallel.
The idea can be easily implemented in MapReduce and experimental results show that the proposed
approach can solve large-scale LSE problems effectively.
ETPL
PDS-033
Buffer Management for Aggregated Streaming Data with Packet Dependencies
Abstract: In many applications, the traffic traversing the network has interpacket dependencies due to
application-level encoding schemes. For some applications, e.g., multimedia streaming, dropping a single
packet may render useless the delivery of a whole sequence. In such environments, the algorithm used to
decide which packet to drop in case of buffer overflows must be carefully designed, to avoid goodput
degradation. We present a model that captures such interpacket dependencies, and design algorithms for
performing packet discard. Traffic consists of an aggregation of multiple streams, each of which consists
of a sequence of interdependent packets. We provide two guidelines for designing buffer management
algorithms, and demonstrate their effectiveness. We devise an algorithm according to these guidelines and
evaluate its performance analytically, using competitive analysis. We also perform a simulation study that
shows that the performance of our algorithm is within a small fraction of the performance of the best
known offline algorithm.
ETPL
PDS-034
Design and Performance Evaluation of Overhearing-Aided Data Caching in Wireless
Ad Hoc Networks
Abstract: Wireless ad hoc network is a promising networking technology to provide users with Internet
access anywhere anytime. To cope with resource constraints of wireless ad hoc networks, data caching is
widely used to efficiently reduce data access cost. In this paper, we propose an efficient data caching
algorithm which makes use of the overhearing property of wireless communication to improve caching
performance. Due to the broadcast nature of wireless links, a packet can be overheard by a node within
the transmission range of the transmitter, even if the node is not the intended target. Our proposed
algorithm explores the overheard information, including data request and data reply, to optimize cache
placement and cache discovery. To the best of our knowledge, this is the first work that considers the
overhearing property of wireless communications in data caching. The simulation results show that,
compared with one representative algorithm and a naive overhearing algorithm, our proposed algorithm
can significantly reduce both message cost and access delay.
ETPL
PDS-035
Dynamic Optimization of Multiattribute Resource Allocation in Self-Organizing
Clouds
Abstract: By leveraging virtual machine (VM) technology which provides performance and fault
isolation, cloud resources can be provisioned on demand in a fine grained, multiplexed manner rather than

in monolithic pieces. By integrating volunteer computing into cloud architectures, we envision a gigantic
self-organizing cloud (SOC) being formed to reap the huge potential of untapped commodity computing
power over the Internet. Toward this new architecture where each participant may autonomously act as
both resource consumer and provider, we propose a fully distributed, VM-multiplexing resource
allocation scheme to manage decentralized resources. Our approach not only achieves maximized
resource utilization using the proportional share model (PSM), but also delivers provably and adaptively
optimal execution efficiency. We also design a novel multiattribute range query protocol for locating
qualified nodes. Contrary to existing solutions which often generate bulky messages per request, our
protocol produces only one lightweight query message per task on the Content Addressable Network
(CAN). It works effectively to find for each task its qualified resources under a randomized policy that
mitigates the contention among requesters. We show the SOC with our optimized algorithms can make an
improvement by 15-60 percent in system throughput than a P2P Grid model. Our solution also exhibits
fairly high adaptability in a dynamic node-churning environment.
ETPL
PDS-036
Enabling Efficient WiFi-Based Vehicular Content Distribution
Abstract: For better road safety and driving experience, content distribution for vehicle users through
roadside Access Points (APs) becomes an important and promising complement to 3G and other cellular
networks. In this paper, we introduce Cooperative Content Distribution System for Vehicles (CCDSV)
which operates upon a network of infrastructure APs to collaboratively distribute contents to moving
vehicles. CCDSV solves several important issues in a practical system, like the robustness to mobility
prediction errors, limited resources of APs and the shared content distribution. Our system organizes the
cooperative APs into a novel structure, namely, the contact map which is based on the vehicular contact
patterns observed by APs. To fully utilize the wireless bandwidth provided by APs, we propose a
representative-based prefetching mechanism, in which a set of representative APs are carefully selected
and then share their prefetched data with others. The selection process explicitly takes into account the
AP's storage capacity, storage status, inter-APs bandwidth and traffic loads on the backhaul links. We
apply network coding in CCDSV to augment the distribution of shared contents. The selection of shared
contents to be prefetched on an AP is based on the storage status of neighboring APs in the contact map
in order to increase the information utility of each prefetched data piece. Through extensive simulations,
CCDSV proves its effectiveness in vehicular content distribution under various scenarios
ETPL
PDS-037
Flexible Symmetrical Global-Snapshot Algorithms for Large-Scale Distributed
Systems
Abstract: Most existing global-snapshot algorithms in distributed systems use control messages to
coordinate the construction of a global snapshot among all processes. Since these algorithms typically
assume the underlying logical overlay topology is fully connected, the number of control messages
exchanged among the whole processes is proportional to the square of number of processes, resulting in
higher possibility of network congestion. Hence, such algorithms are neither efficient nor scalable for a
large-scale distributed system composed of a huge number of processes. Recently, some efforts have been
presented to significantly reduce the number of control messages, but doing so incurs higher response
time instead. In this paper, we propose an efficient global-snapshot algorithm able to let every process
finish its local snapshot in a given number of rounds. Particularly, such an algorithm allows a tradeoff
between the response time and the message complexity. Moreover, our global-snapshot algorithm is
symmetrical in the sense that identical steps are executed by every process. This means that our algorithm

is able to achieve better workload balance and less network congestion. Most importantly, based on our
framework, we demonstrate that the minimum number of control messages required by a symmetrical
global-snapshot algorithm is Ω(N log N), where N is the number of processes. Finally, we also assume
non-FIFO channels.
ETPL
PDS-038
Hardware Signature Designs to Deal with Asymmetry in Transactional Data Sets
Abstract: Transactional Memory (TM) systems must track memory accesses made by concurrent
transactions in order to detect conflicts. Many TM implementations use signatures for this purpose, which
summarize reads and writes in fixed-size bit registers at the cost of false positives (detection of
nonexisting conflicts). Signatures are commonly implemented as two separate same-sized Bloom filters,
one for reads and other for writes. In contrast, transactions frequently exhibit read and write sets of
uneven cardinality. This mismatch between data sets and filter storage introduces inefficiencies in the use
of signatures that have some impact on performance. This paper presents different signature designs as
alternatives to the common scheme to deal with the asymmetry in transactional data sets in an effective
way. Basically, we analyze two classes of new signatures, called multiset and reconfigurable asymmetric
signatures. The first class uses only one Bloom filter to track both read and write sets, while the second
class uses Bloom filters of configurable size for reads and writes. The main focus of this paper is a
thorough study of these alternative signature designs, including a statistical analysis of false positives and
an experimental evaluation, providing performance results and hardware area, time and energy
requirements.
ETPL
PDS-039
Improve Efficiency and Reliability in Single-Hop WSNs with Transmit-Only Nodes
Abstract: Wireless Sensor Networks (WSNs) will play a significant role at the “edge” of the future
“Internet of Things.” In particular, WSNs with transmit-only nodes are attracting more attention due to
their advantages in supporting applications requiring dense and long-lasting deployment at a very low
cost and energy consumption. However, the lack of receivers in transmit-only nodes renders most existing
MAC protocols invalid. Based on our previous study on WSNs with pure transmit-only nodes, this work
proposes a simple, yet cost effective and powerful single-hop hybrid WSN cluster architecture that
contains not only transmit-only nodes but also standard nodes (with transceivers). Along with the hybrid
architecture, this work also proposes a new MAC layer protocol framework called Robust Asynchronous
Resource Estimation (RARE) that efficiently and reliably manages the densely deployed single-hop
hybrid cluster in a self-organized fashion. Through analysis and extensive simulations, the proposed
framework is shown to meet or exceed the needs of most applications in terms of the data delivery
probability, QoS differentiation, system capacity, energy consumption, and reliability. To the best of our
knowledge, this work is the first that brings reliable scheduling to WSNs containing both
nonsynchronized transmit-only nodes and standard nodes.
ETPL
PDS-040
Improving the Reliability of MPI Libraries via Message Flow Checking
Abstract: Distributed processing through ad hoc and sensor networks is having a major impact on scale
and applications of computing. The creation of new cyber-physical services based on wireless sensor
devices relies heavily on how well communication protocols can be adapted and optimized to meet

quality constraints under limited energy resources. The IEEE 802.15.4 medium access control protocol
for wireless sensor networks can support energy efficient, reliable, and timely packet transmission by a
parallel and distributed tuning of the medium access control parameters. Such a tuning is difficult,
because simple and accurate models of the influence of these parameters on the probability of successful
packet transmission, packet delay, and energy consumption are not available. Moreover, it is not clear
how to adapt the parameters to the changes of the network and traffic regimes by algorithms that can run
on resource-constrained devices. In this paper, a Markov chain is proposed to model these relations by
simple expressions without giving up the accuracy. In contrast to previous work, the presence of limited
number of retransmissions, acknowledgments, unsaturated traffic, packet size, and packet copying delay
due to hardware limitations is accounted for. The model is then used to derive a distributed adaptive
algorithm for minimizing the power consumption while guaranteeing a given successful packet reception
probability and delay constraints in the packet transmission. The algorithm does not require any
modification of the IEEE 802.15.4 medium access control and can be easily implemented on network
devices. The algorithm has been experimentally implemented and evaluated on a testbed with off-the-
shelf wireless sensor devices. Experimental results show that the analysis is accurate, that the proposed
algorithm satisfies reliability and delay constraints, and that the approach reduces the energy consumption
of the network under both stationary and transient conditions. Specif- cally, even if the number of devices
and traffic configuration change sharply, the proposed parallel and distributed algorithm allows the
system to operate close to its optimal state by estimating the busy channel and channel access
probabilities. Furthermore, results indicate that the protocol reacts promptly to errors in the estimation of
the number of devices and in the traffic load that can appear due to device mobility. It is also shown that
the effect of imperfect channel and carrier sensing on system performance heavily depends on the traffic
load and limited range of the protocol parameters.
ETPL
PDS-041
Optimal Client-Server Assignment for Internet Distributed Systems
Abstract: We investigate an underlying mathematical model and algorithms for optimizing the
performance of a class of distributed systems over the Internet. Such a system consists of a large number
of clients who communicate with each other indirectly via a number of intermediate servers. Optimizing
the overall performance of such a system then can be formulated as a client-server assignment problem
whose aim is to assign the clients to the servers in such a way to satisfy some prespecified requirements
on the communication cost and load balancing. We show that 1) the total communication load and load
balancing are two opposing metrics, and consequently, their tradeoff is inherent in this class of distributed
systems; 2) in general, finding the optimal client-server assignment for some prespecified requirements on
the total load and load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex
optimization for finding the approximate solution. Our simulation results indicate that the proposed
algorithm produces superior performance than other heuristics, including the popular Normalized Cuts
algorithm.
ETPL
PDS-042
Resilient Self-Compressive Monitoring for Large-Scale Hosting Infrastructures
Abstract: Large-scale hosting infrastructures have become the fundamental platforms for many real-world
systems such as cloud computing infrastructures, enterprise data centers, and massive data processing
systems. However, it is a challenging task to achieve both scalability and high precision while monitoring
a large number of intranode and internode attributes (e.g., CPU usage, free memory, free disk, internode
network delay). In this paper, we present the design and implementation of a Resilient self-Compressive
Monitoring (RCM) system for large-scale hosting infrastructures. RCM achieves scalable distributed
monitoring by performing online data compression to reduce remote data collection cost. RCM provides
failure resilience to achieve robust monitoring for dynamic distributed systems where host and network

failures are common. We have conducted extensive experiments using a set of real monitoring data from
NCSU's virtual computing lab (VCL), PlanetLab, a Google cluster, and real Internet traffic matrices. The
experimental results show that RCM can achieve up to 200 percent higher compression ratio and several
orders of magnitude less overhead than the existing approaches.
ETPL
PDS-043
Service Provision Control in Federated Service Providing Systems
Abstract: Different from traditional P2P systems, individuals nodes of a Federated Service Providing
(FSP) system play a more active role by offering a variety of domain-specific services. The service
provision control (SPC) problem is an important problem of the FSP system and will be tackled in this
paper within a stochastic optimization framework through several steps. The first step focuses on using
stochastic differential equations (SDEs) to model and analyze the dynamic evolution of the service
demand. Driven by the SDE model, expected future performance of a FSP system is analytically
evaluated in the second step. Step three utilizes the differential evolution (DE) algorithm to identify near-
optimal service-providing policies for each node. The service subscription protocol is further proposed in
step four to help every node adjust its local policy in accordance with the services provided by other
nodes. The four steps together implement a complete solution of the SPC problem and will be called the
SDE-based service-provision control (SSPC) mechanism in this paper. Experimental evaluation of the
mechanism has been reported in the paper. The results show that our approach is effective in tackling the
SPC problem and may be therefore suitable for many practical applications.
ETPL
PDS-044
Social Similarity Favors Cooperation: The Distributed Content Replication Case
Abstract: This paper explores how the degree of similarity within a social group can dictate the behavior
of the individual nodes, so as to best tradeoff the individual with the social benefit. More specifically, we
investigate the impact of social similarity on the effectiveness of content placement and dissemination.
We consider three schemes that represent well the spectrum of behavior-shaped content storage strategies:
the selfish, the self-aware cooperative, and the optimally altruistic ones. Our study shows that when the
social group is tight (high degree of similarity), the optimally altruistic behavior yields the best
performance for both the entire group (by definition) and the individual nodes (contrary to typical
expectations). When the group is made up of members with almost no similarity, altruism or cooperation
cannot bring much benefit to either the group or the individuals and thus, selfish behavior emerges as the
preferable choice due to its simplicity. Notably, from a theoretical point of view, our “similarity favors
cooperation” argument is inline with sociological interpretations of human altruistic behavior. On a more
practical note, the self-aware cooperative behavior could be adopted as an easy to implement distributed
alternative to the optimally altruistic one; it has close to the optimal performance for tight social groups
and the additional advantage of not allowing mistreatment of any node, i.e., its induced content retrieval
cost is always smaller than the cost of the selfish strategy.
ETPL
PDS-045
SPOC: A Secure and Privacy-Preserving Opportunistic Computing Framework for
Mobile-Healthcare Emergency
Abstract: With the pervasiveness of smart phones and the advance of wireless body sensor networks
(BSNs), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider into a
pervasive environment for better health monitoring, has attracted considerable interest recently. However,
the flourish of m-Healthcare still faces many challenges including information security and privacy
preservation. In this paper, we propose a secure and privacy-preserving opportunistic computing
framework, called SPOC, for m-Healthcare emergency. With SPOC, smart phone resources including
computing power and energy can be opportunistically gathered to process the computing-intensive
personal health information (PHI) during m-Healthcare emergency with minimal privacy disclosure. In

specific, to leverage the PHI privacy disclosure and the high reliability of PHI process and transmission in
m-Healthcare emergency, we introduce an efficient user-centric privacy access control in SPOC
framework, which is based on an attribute-based access control and a new privacy-preserving scalar
product computation (PPSPC) technique, and allows a medical user to decide who can participate in the
opportunistic computing to assist in processing his overwhelming PHI data. Detailed security analysis
shows that the proposed SPOC framework can efficiently achieve user-centric privacy access control in
m-Healthcare emergency. In addition, performance evaluations via extensive simulations demonstrate the
SPOC's effectiveness in term of providing high-reliable-PHI process and transmission while minimizing
the privacy disclosure during m-Healthcare emergency.
ETPL
PDS-046
A Secure Protocol for Spontaneous Wireless Ad Hoc Networks Creation
Abstract: This paper presents a secure protocol for spontaneous wireless ad hoc networks which uses an
hybrid symmetric/asymmetric scheme and the trust between users in order to exchange the initial data and
to exchange the secret keys that will be used to encrypt the data. Trust is based on the first visual contact
between users. Our proposal is a complete self-configured secure protocol that is able to create the
network and share secure services without any infrastructure. The network allows sharing resources and
offering new services among users in a secure environment. The protocol includes all functions needed to
operate without any external support. We have designed and developed it in devices with limited
resources. Network creation stages are detailed and the communication, protocol messages, and network
management are explained. Our proposal has been implemented in order to test the protocol procedure
and performance. Finally, we compare the protocol with other spontaneous ad hoc network protocols in
order to highlight its features and we provide a security analysis of the system.
ETPL
PDS-047
Bayesian-Inference-Based Recommendation in Online Social Networks
Abstract: In this paper, we propose a Bayesian-inference-based recommendation system for online social
networks. In our system, users share their content ratings with friends. The rating similarity between a
pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A
user propagates a content rating query along the social network to his direct and indirect friends. Based on
the query responses, a Bayesian network is constructed to infer the rating of the querying user. We
develop distributed protocols that can be easily implemented in online social networks. We further
propose to use Prior distribution to cope with cold start and rating sparseness. The proposed algorithm is
evaluated using two different online rating data sets of real users. We show that the proposed Bayesian-
inference-based recommendation is better than the existing trust-based recommendations and is
comparable to Collaborative Filtering (CF) recommendation. It allows the flexible tradeoffs between
recommendation quality and recommendation quantity. We further show that informative Prior
distribution is indeed helpful to overcome cold start and rating sparseness.
ETPL
PDS-048
CDS-Based Virtual Backbone Construction with Guaranteed Routing Cost in Wireless
Sensor Networks
Abstract: Inspired by the backbone concept in wired networks, virtual backbone is expected to bring
substantial benefits to routing in wireless sensor networks (WSNs). Virtual backbone construction based
on Connected Dominating Set (CDS) is a competitive approach among the existing methods used to
establish virtual backbone in WSNs. Traditionally, CDS size was the only factor considered in the CDS-
based approach. The motivation was that smaller CDS leads to simplified network maintenance.

However, routing cost in terms of routing path length is also an important factor for virtual backbone
construction. In our research, both of these two factors are taken into account. Specifically, we attempt to
devise a polynomial-time constant-approximation algorithm that leads to a CDS with bounded CDS size
and guaranteed routing cost. We prove that, under general graph model, there is no polynomial-time
constant-approximation algorithm unless P = NP. Under Unit Disk Graph (UDG) model, we propose an
innovative polynomial-time constant-approximation algorithm, GOC-MCDS-C, that produces a CDS D
whose size I D is within a constant factor from that of the minimum CDS. In addition, for each node pair
u and v, there exists a routing path with all intermediate nodes in D and path length at most 7 · d(u, v),
where d(u, v) is the length of the shortest path between u and v. Our theoretical analysis and simulation
results show that the distributed version of the proposed algorithm, GOC-MCDS-D, outperforms the
existing approaches.
ETPL
PDS-049
Characterization and Management of Popular Content in KAD
Abstract: The endeavor of this work is to study the impact of content popularity in a large-scale Peer-to-
Peer network, namely KAD. Based on an extensive measurement campaign, we pinpoint several
deficiencies of KAD in handling popular content and provide a series of improvements to address such
shortcomings. Our work reveals that keywords, which are associated with content, may become popular
for two distinct reasons. First, we show that some keywords are intrinsically popular because they are
common to many disparate contents: in such case we ameliorate KAD by introducing a simple
mechanism that identifies stopwords. Then, we focus on keyword popularity that directly relates to
popular content. We design and evaluate an adaptive load balancing mechanism that is backward
compatible with the original implementation of KAD. Our scheme features the following properties: 1) it
drives the process that selects the location of peers responsible to store references to objects, based on
object popularity; 2) it solves problems related to saturated peers that would otherwise inflict a significant
drop in the diversity of references to objects, and 3) if coupled with a load-aware content search
procedure, it allows for a more fair and efficient usage of peer resources.
ETPL
PDS-050
Complete EAP Method: User Efficient and Forward Secure Authentication Protocol
for IEEE 802.11 Wireless LANs
Abstract: It is necessary to authenticate users who attempt to access resources in Wireless Local Area
Networks (WLANs). Extensible Authentication Protocol (EAP) is an authentication framework widely
used in WLANs. Authentication mechanisms built on EAP are called EAP methods. The requirements for
EAP methods in WLAN authentication have been defined in RFC 4017. To achieve user efficiency and
robust security, lightweight computation and forward secrecy, excluded in RFC 4017, are desired in
WLAN authentication. However, all EAP methods and authentication protocols designed for WLANs so
far do not satisfy all of the above properties. This manuscript will present a complete EAP method that
utilizes stored secrets and passwords to verify users so that it can 1) fully meet the requirements of RFC
4017, 2) provide for lightweight computation, and 3) allow for forward secrecy. In addition, we also
demonstrate the security of our proposed EAP method with formal proofs.
ETPL
PDS-051
Coordinated Self-Configuration of Virtual Machines and Appliances Using a Model-
Free Learning Approach
Abstract: Cloud computing has a key requirement for resource configuration in a real-time manner. In
such virtualized environments, both virtual machines (VMs) and hosted applications need to be
configured on-the-fly to adapt to system dynamics. The interplay between the layers of VMs and
applications further complicates the problem of cloud configuration. Independent tuning of each aspect

may not lead to optimal system wide performance. In this paper, we propose a framework, namely
CoTuner, for coordinated configuration of VMs and resident applications. At the heart of the framework
is a model-free hybrid reinforcement learning (RL) approach, which combines the advantages of Simplex
method and RL method and is further enhanced by the use of system knowledge guided exploration
policies. Experimental results on Xen-based virtualized environments with TPC-W and TPC-C
benchmarks demonstrate that CoTuner is able to drive a virtual server cluster into an optimal or near-
optimal configuration state on the fly, in response to the change of workload. It improves the systems
throughput by more than 30 percent over independent tuning strategies. In comparison with the
coordinated tuning strategies based on basic RL or Simplex algorithm, the hybrid RL algorithm gains 25
to 40 percent throughput improvement.
ETPL
PDS-052
Exploiting Concurrency for Efficient Dissemination in Wireless Sensor Networks
Abstract: Cloud computing has a key requirement for resource configuration in a real-time manner. In
such virtualized environments, both virtual machines (VMs) and hosted applications need to be
configured on-the-fly to adapt to system dynamics. The interplay between the layers of VMs and
applications further complicates the problem of cloud configuration. Independent tuning of each aspect
may not lead to optimal system wide performance. In this paper, we propose a framework, namely
CoTuner, for coordinated configuration of VMs and resident applications. At the heart of the framework
is a model-free hybrid reinforcement learning (RL) approach, which combines the advantages of Simplex
method and RL method and is further enhanced by the use of system knowledge guided exploration
policies. Experimental results on Xen-based virtualized environments with TPC-W and TPC-C
benchmarks demonstrate that CoTuner is able to drive a virtual server cluster into an optimal or near-
optimal configuration state on the fly, in response to the change of workload. It improves the systems
throughput by more than 30 percent over independent tuning strategies. In comparison with the
coordinated tuning strategies based on basic RL or Simplex algorithm, the hybrid RL algorithm gains 25
to 40 percent throughput improvement.
ETPL
PDS-053
Exploiting Concurrency for Efficient Dissemination in Wireless Sensor Networks
Abstract: Wireless sensor networks (WSNs) can be successfully applied in a wide range of applications.
Efficient data dissemination is a fundamental service which enables many useful high-level functions
such as parameter reconfiguration, network reprogramming, etc. Many current data dissemination
protocols employ network coding techniques to deal with packet losses. The coding overhead, however,
becomes a bottleneck in terms of dissemination delay. We exploit the concurrency potential of sensor
nodes and propose MT-Deluge, a multithreaded design of a coding-based data dissemination protocol. By
separating the coding and radio operations into two threads and carefully scheduling their executions,
MT-Deluge shortens the dissemination delay effectively. An incremental decoding algorithm is employed
to further improve MT-Deluge's performance. Experiments with 24 TelosB motes on four representative
topologies show that MT-Deluge shortens the dissemination delay by 25.5-48.6 percent compared to a
typical data dissemination protocol while keeping the merits of loss resilience.
ETPL
PDS-054
Fault Tolerance in Distributed Systems Using Fused Data Structures

Abstract: Replication is the prevalent solution to tolerate faults in large data structures hosted on
distributed servers. To tolerate f crash faults (dead/unresponsive data structures) among n distinct data
structures, replication requires f + 1 replicas of each data structure, resulting in nf additional backups. We
present a solution, referred to as fusion that uses a combination of erasure codes and selective replication
to tolerate f crash faults using just f additional fused backups. We show that our solution achieves O(n)
savings in space over replication. Further, we present a solution to tolerate f Byzantine faults (malicious
data structures), that requires only nf + f backups as compared to the 2nf backups required by replication.
We explore the theory of fused backups and provide a library of such backups for all the data structures in
the Java Collection Framework. The theoretical and experimental evaluation confirms that the fused
backups are space-efficient as compared to replication, while they cause very little overhead for normal
operation. To illustrate the practical usefulness of fusion, we use fused backups for reliability in Amazon's
highly available key-value store, Dynamo. While the current replication-based solution uses 300 backup
structures, we present a solution that only requires 120 backup structures. This results in savings in space
as well as other resources such as power.
ETPL
PDS-055
Feasibility of Polynomial-Time Randomized Gathering for Oblivious Mobile Robots
Abstract: We consider the problem of gathering n anonymous and oblivious mobile robots, which
requires that all robots meet in finite time at a nonpredefined point. While the gathering problem cannot
be solved deterministically without assuming any additional capabilities for the robots, randomized
approaches easily allow it to be solvable. However, the randomized solutions currently known have a
time complexity that is exponential in n with no additional assumption. This fact yields the following two
questions: Is it possible to construct a randomized gathering algorithm with polynomial expected time? If
it is not possible, what is the minimal additional assumption necessary to obtain such an algorithm? In
this paper, we address these questions from the aspect of multiplicity-detection capabilities. We newly
introduce two weaker variants of multiplicity detection, called local-strong and local-weak multiplicity,
and investigate whether those capabilities permit a gathering algorithm with polynomial expected time or
not. The contribution of this paper is to show that any algorithm only assuming local-weak multiplicity
detection takes exponential number of rounds in expectation. On the other hand, we can obtain a constant-
round gathering algorithm using local-strong multiplicity detection. These results imply that the two
models of multiplicity detection are significantly different in terms of their computational power.
Interestingly, these differences disappear if we take one more assumption that all robots are scattered (i.e.,
no two robots stay at the same location) initially. We can obtain a gathering algorithm that takes a
constant number of rounds in expectation, assuming local-weak multiplicity detection and scattered initial
configurations.
ETPL
PDS-056
Finding All Maximal Contiguous Subsequences of a Sequence of Numbers in O(1)
Communication Rounds
Abstract: Given a sequence A of real numbers, we wish to find a list of all nonoverlapping contiguous
subsequences of A that are maximal. A maximal subsequence M of A has the property that no proper
subsequence of M has a greater sum of values. Furthermore, M may not be contained properly within any
subsequence of A with this property. This problem has several applications in Computational Biology and
can be solved sequentially in linear time. We present a BSP/CGM algorithm that solves this problem
using p processors in O(|A|=p) time and O(|A|=p) space per processor. The algorithm uses a constant
number of communication rounds of size at most O(|A|=p). Thus, the algorithm achieves linear speedup

and is highly scalable. To our knowledge, there are no previous known parallel BSP/CGM algorithms to
solve this problem.
ETPL
PDS-057
Geocommunity-Based Broadcasting for Data Dissemination in Mobile Social Networks
Abstract: In this paper, we consider the issue of data broadcasting in mobile social networks (MSNets).
The objective is to broadcast data from a superuser to other users in the network. There are two main
challenges under this paradigm, namely 1) how to represent and characterize user mobility in realistic
MSNets; 2) given the knowledge of regular users' movements, how to design an efficient superuser route
to broadcast data actively. We first explore several realistic data sets to reveal both geographic and social
regularities of human mobility, and further propose the concepts of geocommunity and geocentrality into
MSNet analysis. Then, we employ a semi-Markov process to model user mobility based on the
geocommunity structure of the network. Correspondingly, the geocentrality indicating the “dynamic user
density” of each geocommunity can be derived from the semi-Markov model. Finally, considering the
geocentrality information, we provide different route algorithms to cater to the superuser that wants to
either minimize total duration or maximize dissemination ratio. To the best of our knowledge, this work is
the first to study data broadcasting in a realistic MSNet setting. Extensive trace-driven simulations show
that our approach consistently outperforms other existing superuser route design algorithms in terms of
dissemination ratio and energy efficiency.
ETPL
PDS-058
LOBOT: Low-Cost, Self-Contained Localization of Small-Sized Ground Robotic
Vehicles
Abstract: It is often important to obtain the real-time location of a small-sized ground robotic vehicle
when it performs autonomous tasks either indoors or outdoors. We propose and implement LOBOT, a
low-cost, self-contained localization system for small-sized ground robotic vehicles. LOBOT provides
accurate real-time, 3D positions in both indoor and outdoor environments. Unlike other localization
schemes, LOBOT does not require external reference facilities, expensive hardware, careful tuning or
strict calibration, and is capable of operating under various indoor and outdoor environments. LOBOT
identifies the local relative movement through a set of integrated inexpensive sensors and well corrects
the localization drift by infrequent GPS-augmentation. Our empirical experiments in various temporal and
spatial scales show that LOBOT keeps the positioning error well under an accepted threshold.
ETPL
PDS-059
Lower Bound for Node Buffer Size in Intermittently Connected Wireless Networks
Abstract: We study the fundamental lower bound for node buffer size in intermittently connected wireless
networks. The intermittent connectivity is caused by the possibility of node inactivity due to some
external constraints. We find even with infinite channel capacity and node processing speed, buffer
occupation in each node does not approach zero in a static random network where each node keeps a
constant message generation rate. Given the condition that each node has the same probability p of being
inactive during each time slot, there exists a critical value pc(λ) for this probability from a percolation-
based perspective. When p <; pc(λ), the network is in the supercritical case, and there is an achievable
lower bound (In our paper, “achievable” means that node buffer size in networks can achieve the same
order as the lower bound by applying some transmission scheme) for the occupied buffer size of each
node, which is asymptotically independent of the size of the network. If p > pc(λ), the network is in the

subcritical case, and there is a tight lower bound Θ(√n) for buffer occupation, where n is the number of
nodes in the network.
ETPL
PDS-060
On-Chip Sensor Network for Efficient Management of Power Gating-Induced
Power/Ground Noise in Multiprocessor System on Chip
Abstract: Reducing feature sizes and power supply voltage allows integrating more processing units
(PUs) on multiprocessor system on chip (MPSoC) to satisfy the increasing demands of applications.
However, it also makes MPSoC more susceptible to various reliability threats, such as high temperature
and power/ground (P/G) noise. As the scale and complexity of MPSoC continuously increase, monitoring
and mitigating reliability threats at runtime could offer better performance, scalability, and flexibility for
MPSoC designs. In this paper, we propose a systematic approach, on-chip sensor network (SENoC), to
collaboratively predict, detect, report, and alleviate runtime threats in MPSoC. SENoC not only detects
reliability threats and shares related information among PUs, but also plans and coordinates the reactions
of related PUs in MPSoC. SENoC is used to alleviate the impacts of simultaneous switching noise in
MPSoC's P/G network during power gating. Based on the detailed noise behaviors under different
scenarios derived by our circuit-level MPSoC P/G noise simulation and analysis platform, simulation
results show that SENoC helps to achieve on average 26.2 percent performance improvement compared
with the traditional stop-go method with 1.4 percent area overhead in an 8*8-core MPSoC in 45 nm. An
architecture-level cycle-accurate simulator based on SystemC is implemented to study the performance of
the proposed SENoC. By applying sophisticated scheduling techniques to optimize the total system
performance, a higher performance improvement of 43.5 percent is achieved for a set of real-life
applications.
ETPL
PDS-061
Robust Tracking of Small-Scale Mobile Primary User in Cognitive Radio Networks
Abstract: In cognitive radio networks (CRNs), secondary users must be able to accurately and reliably
track the location of small-scale mobile primary users/devices (e.g., wireless microphones) in order to
efficiently utilize spatial spectrum opportunities, while protecting primary communications. However,
accurate tracking of the location of mobile primary users is difficult due mainly to the CR-unique
constraint, i.e., localization must rely solely on reported sensing results (i.e., measured primary signal
strengths), which can easily be compromised by malicious sensors (or attackers). To cope with this
challenge, we propose a new framework, called Sequential mOnte carLo combIned with shadow-faDing
estimation (SOLID), for accurate, attack/fault-tolerant tracking of small-scale mobile primary users. The
key idea underlying SOLID is to exploit the temporal shadow fading correlation in sensing results
induced by the primary user's mobility. Specifically, SOLID augments conventional Sequential Monte
Carlo (SMC)-based target tracking with shadow-fading estimation. By examining the shadow-fading gain
between the primary transmitter and CRs/sensors, SOLID 1) significantly improves the accuracy of
primary tracking regardless of the presence/absence of attack, and 2) successfully masks the abnormal
sensing reports due to sensor faults or attacks, preserving localization accuracy and improving spatial
spectrum efficiency. Our extensive evaluation in realistic wireless fading environments shows that
SOLID lowers localization error by up to 88 percent in the absence of attacks, and 89 percent in the
presence of the challenging "slow-poisoning” attack, compared to the conventional SMC-based tracking.

ETPL
PDS-062
Scheduling Sensor Data Collection with Dynamic Traffic Patterns
Abstract: The network traffic pattern of continuous sensor data collection often changes constantly over
time due to the exploitation of temporal and spatial data correlations as well as the nature of condition-
based monitoring applications. In contrast to most existing TDMA schedules designed for a static
network traffic pattern, this paper proposes a novel TDMA schedule that is capable of efficiently
collecting sensor data for any network traffic pattern and is thus well suited to continuous data collection
with dynamic traffic patterns. In the proposed schedule, the energy consumed by sensor nodes for any
traffic pattern is very close to the minimum required by their workloads given in the traffic pattern. The
schedule also allows the base station to conclude data collection as early as possible according to the
traffic load, thereby reducing the latency of data collection. We present a distributed algorithm for
constructing the proposed schedule. We develop a mathematical model to analyze the performance of the
proposed schedule. We also conduct simulation experiments to evaluate the performance of different
schedules using real-world data traces. Both the analytical and simulation results show that, compared
with existing schedules that are targeted on a fixed traffic pattern, our proposed schedule significantly
improves the energy efficiency and time efficiency of sensor data collection with dynamic traffic patterns.
ETPL
PDS-063
Secure SOurce-BAsed Loose Synchronization (SOBAS) for Wireless Sensor Networks
Abstract: We present the Secure SOurce-BAsed Loose Synchronization (SOBAS) protocol to securely
synchronize the events in the network, without the transmission of explicit synchronization control
messages. In SOBAS, nodes use their local time values as a one-time dynamic key to encrypt each
message. In this way, SOBAS provides an effective dynamic en-route filtering mechanism, where the
malicious data is filtered from the network. With SOBAS, we are able to achieve our main goal of
synchronizing events at the sink as quickly, as accurately, and as surreptitiously as possible. With loose
synchronization, SOBAS reduces the number of control messages needed for a WSN to operate providing
the key benefits of reduced energy consumption as well as reducing the opportunity for malicious nodes
to eavesdrop, intercept, or be made aware of the presence of the network. Albeit a loose synchronization
per se, SOBAS is also able to provide 7.24 μs clock precision given today's sensor technology, which is
much better than other comparable schemes (schemes that do not employ GPS devices). Also, we show
that by recognizing the need for and employing loose time synchronization, necessary synchronization
can be provided to the WSN application using half of the energy needed for traditional schemes. Both
analytical and simulation results are presented to verify the feasibility of SOBAS as well as the energy
consumption of the scheme under normal operation and attack from malicious nodes.
ETPL
PDS-064
On Data Staging Algorithms for Shared Data Accesses in Clouds
Abstract: In this paper, we study the strategies for efficiently achieving data staging and caching on a set
of vantage sites in a cloud system with a minimum cost. Unlike the traditional research, we do not intend
to identify the access patterns to facilitate the future requests. Instead, with such a kind of information
presumably known in advance, our goal is to efficiently stage the shared data items to predetermined sites
at advocated time instants to align with the patterns while minimizing the monetary costs for caching and
transmitting the requested data items. To this end, we follow the cost and network models in [1] and
extend the analysis to multiple data items, each with single or multiple copies. Our results show that

under homogeneous cost model, when the ratio of transmission cost and caching cost is low, a single copy
of each data item can efficiently serve all the user requests. While in multicopy situation, we also consider
the tradeoff between the transmission cost and caching cost by controlling the upper bounds of
transmissions and copies. The upper bound can be given either on per-item basis or on all-item basis. We
present efficient optimal solutions based on dynamic programming techniques to all these cases provided
that the upper bound is polynomially bounded by the number of service requests and the number of
distinct data items. In addition to the homogeneous cost model, we also briefly discuss this problem under
a heterogeneous cost model with some simple yet practical restrictions and present a 2-approximation
algorithm to the general case. We validate our findings by implementing a data staging solver, whereby
conducting extensive simulation studies on the behaviors of the algorithms.
ETPL
PDS-065
WILL: Wireless Indoor Localization without Site Survey
Abstract: Indoor localization is of great importance for a range of pervasive applications, attracting many
research efforts in the past two decades. Most radio-based solutions require a process of site survey, in
which radio signatures are collected and stored for further comparison and matching. Site survey involves
intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and
leverage user motions to construct radio floor plan that is previously obtained by site survey. On this
basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and
mobile phones. WILL is deployed in a real building covering over 1600 m2, and its deployment is easy
and rapid since site survey is no longer needed. The experiment results show that WILL achieves
competitive performance comparing with traditional approaches.
ETPL
PDS-066
Analysis of a Pool Management Scheme for Cloud Computing Centers
Abstract: In this paper, we propose an analytical performance model that addresses the complexity of
cloud centers through distinct stochastic submodels, the results of which are integrated to obtain the
overall solution. Our model incorporates the important aspects of cloud centers such as pool management,
compound requests (i.e., a set of requests submitted by one user simultaneously), resource virtualization
and realistic servicing steps. In this manner, we obtain not only a detailed assessment of cloud center
performance, but also clear insights into equilibrium arrangement and capacity planning that allows
servicing delays, task rejection probability, and power consumption to be kept under control.
ETPL
PDS-067
A New Progressive Algorithm for a Multiple Longest Common Subsequences Problem
and Its Efficient Parallelization
Abstract: The multiple longest common subsequence (MLCS) problem, which is related to the
measurement of sequence similarity, is one of the fundamental problems in many fields. As an NP-hard
problem, finding a good approximate solution within a reasonable time is important for solving large-size
problems in practice. In this paper, we present a new progressive algorithm, Pro-MLCS, based on the
dominant point approach. Pro-MLCS can find an approximate solution quickly and then progressively
generate better solutions until obtaining the optimal one. Pro-MLCS employs three new techniques: 1) a
new heuristic function for prioritizing candidate points; 2) a novel $(d)$-index-tree data structure for
efficient computation of dominant points; and 3) a new pruning method using an upper bound function
and approximate solutions. Experimental results show that Pro-MLCS can obtain the first approximate
solution almost instantly and needs only a very small fraction, e.g., 3 percent, of the entire running time to

get the optimal solution. Compared to existing state-of-the-art algorithms, Pro-MLCS can find better
solutions in much shorter time, one to two orders of magnitude faster. In addition, two parallel versions of
Pro-MLCS are developed: DPro-MLCS for distributed memory architecture and DSDPro-MLCS for
hierarchical distributed shared memory architecture. Both parallel algorithms can efficiently utilize
parallel computing resources and achieve nearly linear speedups. They also have a desirable
progressiveness property—finding better solutions in shorter time when given more hardware
resources.
ETPL
PDS-068
A Novel Message Scheduling Framework for Delay Tolerant Networks Routing
Abstract: Multicopy routing strategies have been considered the most applicable approaches to achieve
message delivery in Delay Tolerant Networks (DTNs). Epidemic routing and two-hop forwarding routing
are two well-reported approaches for delay tolerant networks routing which allow multiple message
replicas to be launched in order to increase message delivery ratio and/or reduce message delivery delay.
This advantage, nonetheless, is at the expense of additional buffer space and bandwidth overhead. Thus,
to achieve efficient utilization of network resources, it is important to come up with an effective message
scheduling strategy to determine which messages should be forwarded and which should be dropped in
case of buffer is full. This paper investigates a new message scheduling framework for epidemic and two-
hop forwarding routing in DTNs, such that the forwarding/dropping decision can be made at a node
during each contact for either optimal message delivery ratio or message delivery delay. Extensive
simulation results show that the proposed message scheduling framework can achieve better performance
than its counterparts.
ETPL
PDS-069
Attribute-Aware Data Aggregation Using Potential-Based Dynamic Routing in
Wireless Sensor Networks
Abstract: The resources especially energy in wireless sensor networks (WSNs) are quite limited. Since
sensor nodes are usually much dense, data sampled by sensor nodes have much redundancy, data
aggregation becomes an effective method to eliminate redundancy, minimize the number of transmission,
and then to save energy. Many applications can be deployed in WSNs and various sensors are embedded
in nodes, the packets generated by heterogenous sensors or different applications have different attributes.
The packets from different applications cannot be aggregated. Otherwise, most data aggregation schemes
employ static routing protocols, which cannot dynamically or intentionally forward packets according to
network state or packet types. The spatial isolation caused by static routing protocol is unfavorable to data
aggregation. To make data aggregation more efficient, in this paper, we introduce the concept of packet
attribute, defined as the identifier of the data sampled by different kinds of sensors or applications, and
then propose an attribute-aware data aggregation (ADA) scheme consisting of a packet-driven timing
algorithm and a special dynamic routing protocol. Inspired by the concept of potential in physics and
pheromone in ant colony, a potential-based dynamic routing is elaborated to support an ADA strategy.
The performance evaluation results in series of scenarios verify that the ADA scheme can make the
packets with the same attribute spatially convergent as much as possible and therefore improve the
efficiency of data aggregation. Furthermore, the ADA scheme also offers other properties, such as
scalable with respect to network size and adaptable for tracking mobile events.
ETPL
PDS-070
DCNS: An Adaptable High Throughput RFID Reader-to-Reader Anticollision Protocol

Abstract: The reader-to-reader collision problem represents a research topic of great recent interest for the
radio frequency identification (RFID) technology. Among the state-of-the-art anticollision protocols, the
ones that provide high throughput often have special requirements, such as extra hardware. This study
investigates new high throughput solutions for static RFID networks without additional requirements. In
this paper, two contributions are presented: a new configuration, called Killer, and a new protocol, called
distributed color noncooperative selection (DCNS). The proposed configuration generates selfish
behavior, thereby increasing channel utilization and throughput. DCNS fully exploits the Killer
configuration and provides new features, such as dynamic priority management, which modifies the
performance of the RFID readers when it is requested. Simulations have been conducted in order to
analyze the effects of the innovations proposed. The proposed approach is especially suitable for low-cost
applications with a priority not uniformly distributed among readers. The experimental analysis has
shown that DCNS provides a greater throughput than the state-of-the-art protocols, even those with
additional requirements (e.g., 16 percent better than NFRA).
ETPL
PDS-071
Finite-Difference Wave Propagation Modeling on Special-Purpose Dataflow Machines
Abstract: Modeling wave propagation through the earth is an important application in geoscience. We
present a framework for wave propagation modeling on special-purpose hardware, which dramatically
improves the application performance compared to conventional CPUs. We utilize custom hardware
platforms consisting of a mix of x86 CPUs and dataflow engines connected by high-bandwidth
communication links. Application programmers describe their algorithms in a domain specific language
using Java syntax, with special dataflow semantics overlayed on top of the Java language. The
application-specific dataflow engines run at hundreds of MHz with massive parallelism and deliver high
performance/Watt, up to 30 times more energy efficient than conventional CPUs. The power efficiency of
this approach suggests that dataflow computing may have a key role to play in the improvements in
power efficiency necessary to reach exascale computing.
ETPL
PDS-072
GKAR: A Novel Geographic $(K)$-Anycast Routing for Wireless Sensor Networks
Abstract: To efficiently archive and query data in wireless sensor networks (WSNs), distributed storage
systems, and multisink schemes have been proposed recently. However, such distributed access cannot be
fully supported and exploited by existing routing protocols in a large-scale WSN. In this paper, we will
address this challenging issue and propose a distributed geographic $(K)$-anycast routing (GKAR)
protocol for WSNs, which can efficiently route data from a source sensor to any $(K)$ destinations (e.g.,
storage nodes or sinks). To guarantee $(K)$-delivery, an iterative approach is adopted in GKAR where in
each round, GKAR will determine not only the next hops at each node, but also a set of potential
destinations for every next hop node to reach. Efficient algorithms are designed to determine the selection
of the next hops and destination set division at each intermediate node. We analyze the complexity of
GKAR in each round and we also theoretically analyze the expected number of rounds required to
guarantee $(K)$-delivery. Simulation results demonstrate the superiority of the GKAP scheme in
reducing the total duration and the communication overhead for finding $(K)$ destinations, by comparing
with the existing schemes, e.g., $(K 1)$-anycast [10].

ETPL
PDS-073
Heterogeneous Resource Allocation under Degree Constraints
Abstract: In this paper, we consider the problem of assigning a set of clients with demands to a set of
servers with capacities and degree constraints. The goal is to find an allocation such that the number of
clients assigned to a server is smaller than the server's degree and their overall demand is smaller than the
server's capacity, while maximizing the overall throughput. This problem has several natural applications
in the context of independent tasks scheduling or virtual machines allocation. We consider both the
offline (when clients are known beforehand) and the online (when clients can join and leave the system at
any time) versions of the problem. We first show that the degree constraint on the maximal number of
clients that a server can handle is realistic in many contexts. Then, our main contribution is to prove that
even if it makes the allocation problem more difficult (NP-Complete), a very small additive resource
augmentation on the servers degree is enough to find in polynomial time a solution that achieves at least
the optimal throughput. After a set of theoretical results on the complexity of the offline and online
versions of the problem, we propose several other greedy heuristics to solve the online problem and we
compare the performance (in terms of throughput) and the cost (in terms of disconnections and
reconnections) of all proposed algorithms through a set of extensive simulation results
ETPL
PDS-074
Lightweight Location Verification Algorithms for Wireless Sensor Networks
Abstract: The knowledge of sensors' locations is crucial information for many applications in Wireless
Sensor Networks (WSNs). When sensor nodes are deployed in hostile environments, the localization
schemes are vulnerable to various attacks, e.g., wormhole attack, pollution attack, range
enlargement/reduction attack, and etc. Therefore, sensors' locations are not trustworthy and need to be
verified before they can be used by location-based applications. Previous verification schemes either
require group-based deployment knowledge of the sensor field, or depend on expensive or dedicated
hardware, thus they cannot be used for low-cost sensor networks. In this paper, we propose a lightweight
location verification system that performs both “on-spot” and “in-
region” location verifications. The on-spot verification intends to verify whether the locations
claimed by sensors are far from their true spots beyond a certain distance. We propose two algorithms that
detect abnormal locations by exploring the inconsistencies between sensors' claimed locations and their
neighborhood observations. The in-region verification verifies whether a sensor is inside an application-
specific verification region. Compared to on-spot verification, the in-region verification is tolerable to
large errors as long as the locations of sensors don't cause the application to malfunction. We study how
to derive the verification region for different applications and design a probabilistic algorithm to compute
in-region confidence for each sensor. Experiment results show that our on-spot and in-region algorithms
can verify sensors' locations with high detection rate and low false positive rate. They are robust in the
presence of malicious attacks that are launched during the verification process. Moreover, compared with
previous verification schemes, our algorithms are effective and lightweight because they do not rely on
the knowledge of deployment of senso- s, and they don't require expensive or dedicated hardware, so our
algorithms can be used in any low-cost sensor networks.
ETPL
PDS-075
Load Rebalancing for Distributed File Systems in Clouds

Abstract: Distributed file systems are key building blocks for cloud computing applications based on the
MapReduce programming paradigm. In such file systems, nodes simultaneously serve computing and
storage functions; a file is partitioned into a number of chunks allocated in distinct nodes so that
MapReduce tasks can be performed in parallel over the nodes. However, in a cloud computing
environment, failure is the norm, and nodes may be upgraded, replaced, and added in the system. Files
can also be dynamically created, deleted, and appended. This results in load imbalance in a distributed file
system; that is, the file chunks are not distributed as uniformly as possible among the nodes. Emerging
distributed file systems in production systems strongly depend on a central node for chunk reallocation.
This dependence is clearly inadequate in a large-scale, failure-prone environment because the central load
balancer is put under considerable workload that is linearly scaled with the system size, and may thus
become the performance bottleneck and the single point of failure. In this paper, a fully distributed load
rebalancing algorithm is presented to cope with the load imbalance problem. Our algorithm is compared
against a centralized approach in a production system and a competing distributed solution presented in
the literature. The simulation results indicate that our proposal is comparable with the existing centralized
approach and considerably outperforms the prior distributed algorithm in terms of load imbalance factor,
movement cost, and algorithmic overhead. The performance of our proposal implemented in the Hadoop
distributed file system is further investigated in a cluster environment.
ETPL
PDS-076
Lower and Upper Bounds for Multicasting under Distance Dependent Forwarding
Cost Functions
Abstract: Assume a forwarding cost function which depends on the sender receiver separation, and
assume further that noncooperative relaying is applied. What is the minimum total forwarding cost
required for sending a message from source to one or more destinations when multicasting along optimal
placed relaying nodes is applied? In this paper, I define and analyze cost function properties from which I
derive general lower bound expressions on multicasting costs. I consider an MAC layer model which does
not exploit the broadcast property of wireless communication and an MAC layer model which exploits it.
For specific cost functions, I show further that in case of optimal relay positions, multicasts can be
constructed whose cost always stays below the derived lower bound expression plus an additive constant
depending on the number of destinations. For both, lower and upper bounds, I define a general procedure
to check if—and if yes how—my findings can be used to derive the specific lower and
upper bound expressions for a given cost function. I explain the procedure with three cost function
examples: the euclidean distance, energy cost function, and the expected number of retransmissions under
Rayleigh fading.
ETPL
PDS-078
PASQUAL: Parallel Techniques for Next Generation Genome Sequence Assembly
Abstract: The study of genomes has been revolutionized by sequencing machines that output many short
overlapping substrings (called reads). The task of sequence assembly in practice is to reconstruct long
contiguous genome subsequences from the reads. With Next Generation Sequencing (NGS) technologies,
assembly software needs to be more accurate, faster, and more memory-efficient due to the problem
complexity and the size of the data sets. In this paper, we develop parallel algorithms and compressed
data structures to address several computational challenges of NGS assembly. We demonstrate how

commonly available multicore architectures can be efficiently utilized for sequence assembly. In all
stages (indexing input strings, string graph construction and simplification, extraction of contiguous
subsequences) of our software Pasqual, we use shared-memory parallelism to speed up the assembly
process. In our experiments with data of up to 6.8 billion base pairs, we demonstrate that Pasqual
generally delivers the best tradeoff between speed, memory consumption, and solution quality. On
synthetic and real data sets Pasqual scales well on our test machine with 40 CPU cores with increasing
number of threads. Given enough cores, Pasqual is fastest in our comparison.
ETPL
PDS-079
Query-Log Aware Replicated Declustering
Abstract: Data declustering and replication can be used to reduce I/O times related with processing of data
intensive queries. Declustering parallelizes the query retrieval process by distributing the data items
requested by queries among several disks. Replication enables alternative disk choices for individual disk
items and thus provides better query parallelism options. In general, existing replicated declustering
schemes do not consider query log information and try to optimize all possible queries for a specific
query type, such as range or spatial queries. In such schemes, it is assumed that two or more copies of all
data items are to be generated and scheduling of these copies to disks are discussed. However, in some
applications, generation of even two copies of all of the data items is not feasible, since data items tend to
have very large sizes. In this work, we assume that there is a given limit on disk capacities and thus on
replication amounts. We utilize existing query-log information to propose a selective replicated
declustering scheme, in which we select the data items to be replicated and decide on their scheduling
onto disks while respecting disk capacities. We propose and implement an iterative improvement
algorithm to obtain a two-way replicated declustering and use this algorithm in a recursive framework to
generate a multiway replicated declustering. Then we improve the obtained multiway replicated
declustering by efficient refinement heuristics. Experiments conducted on realistic data sets show that the
proposed scheme yields better performance results compared to existing replicated declustering schemes.
ETPL
PDS-080
RASS: A Real-Time, Accurate, and Scalable System for Tracking Transceiver-Free
Objects
Abstract: Transceiver-free object tracking is to trace a moving object that does not carry any
communication device in an environment with some monitoring nodes predeployed. Among all the
tracking technologies, RF-based technology is an emerging research field facing many challenges.
Although we proposed the original idea, until now there is no method achieving scalability without
sacrificing latency and accuracy. In this paper, we put forward a real-time tracking system RASS, which
can achieve this goal and is promising in the applications like the safeguard system. Our basic idea is to
divide the tracking field into different areas, with adjacent areas using different communication channels.
So, the interference among different areas can be prevented. For each area, three communicating nodes
are deployed on the ceiling as a regular triangle to monitor this area. In each triangle area, we use a
Support Vector Regression (SVR) model to locate the object. This model simulates the relationship
between the signal dynamics caused by the object and the object position. It not only considers the ideal
case of signal dynamics caused by the object, but also utilizes their irregular information. As a result, it
can reach the tracking accuracy to around 1 m by just using three nodes in a triangle area with 4 m in each
side. The experiments show that the tracking latency of the proposed RASS system is bounded by only
about 0.26 m. Our system scales well to a large deployment field without sacrificing the latency and
accuracy.

ETPL
PDS-081
Retrieving Smith-Waterman Alignments with Optimizations for Megabase Biological
Sequences Using GPU
Abstract: In Genome Projects, biological sequences are aligned thousands of times, in a daily basis. The
Smith-Waterman algorithm is able to retrieve the optimal local alignment with quadratic time and space
complexity. So far, aligning huge sequences, such as whole chromosomes, with the Smith-Waterman
algorithm has been regarded as unfeasible, due to huge computing and memory requirements. However,
high-performance computing platforms such as GPUs are making it possible to obtain the optimal result
for huge sequences in reasonable time. In this paper, we propose and evaluate CUDAlign 2.1, a parallel
algorithm that uses GPU to align huge sequences, executing the Smith-Waterman algorithm combined
with Myers-Miller, with linear space complexity. In order to achieve that, we propose optimizations
which are able to reduce significantly the amount of data processed, while enforcing full parallelism most
of the time. Using the NVIDIA GTX 560 Ti board and comparing real DNA sequences that range from
162 KBP (Thousand Base Pairs) to 59 MBP (Million Base Pairs), we show that CUDAlign 2.1 is
scalable. Also, we show that CUDAlign 2.1 is able to produce the optimal alignment between the
chimpanzee chromosome 22 (33 MBP) and the human chromosome 21 (47 MBP) in 8.4 hours and the
optimal alignment between the chimpanzee chromosome Y (24 MBP) and the human chromosome Y (59
MBP) in 13.1 hours.
ETPL
PDS-082
Scalable and Accurate Graph Clustering and Community Structure Detection
Abstract: One of the most useful measures of cluster quality is the modularity of the partition, which
measures the difference between the number of the edges joining vertices from the same cluster and the
expected number of such edges in a random graph. In this paper, we show that the problem of finding a
partition maximizing the modularity of a given graph $(G)$ can be reduced to a minimum weighted cut
(MWC) problem on a complete graph with the same vertices as $(G)$. We then show that the resulting
minimum cut problem can be efficiently solved by adapting existing graph partitioning techniques. Our
algorithm finds clusterings of a comparable quality and is much faster than the existing clustering
algorithms.
ETPL
PDS-083
Scaling Laws of Cognitive Ad Hoc Networks over General Primary Network Models
Abstract: We study the capacity scaling laws for the cognitive network that consists of the primary hybrid
network (PhN) and secondary ad hoc network (SaN). PhN is further comprised of an ad hoc network and
a base station-based (BS-based) network. SaN and PhN are overlapping in the same deployment region,
operate on the same spectrum, but are independent with each other in terms of communication
requirements. The primary users (PUs), i.e., the ad hoc nodes in PhN, have the priority to access the
spectrum. The secondary users (SUs), i.e., the ad hoc nodes in SaN, are equipped with cognitive radios,
and have the functionalities to sense the idle spectrum and obtain the necessary information of primary
nodes in PhN. We assume that PhN adopts one out of three classical types of strategies, i.e., pure ad hoc
strategy, BS-based strategy, and hybrid strategy. We aim to directly derive multicast capacity for SaN to
unify the unicast and broadcast capacities under two basic principles: 1) The throughput for PhN cannot
be undermined in order sense due to the presence of SaN. 2) The protocol adopted by PhN does not alter
in the interest of SaN, anyway. Depending on which type of strategy is adopted in PhN, we design the
optimal-throughput strategy for SaN. We show that there exists a threshold of the density of SUs

according to the density of PUs beyond which it can be proven that: 1) when PhN adopts the pure ad hoc
strategy or hybrid strategy, SaN can achieve the multicast capacity of the same order as it is stand-alone;
2) when PhN adopts the BS-based strategy, SaN can asymptotically achieve the multicast capacity of the
same order as if PhN were absent, if some specific conditions in terms of relations among the numbers of
SUs, PUs, the destinations of each multicast session in SaN, and BSs in PhN hold.
ETPL
PDS-084
SyRaFa: Synchronous Rate and Frequency Adjustment for Utilization Control in
Distributed Real-Time Embedded Systems
Abstract: To efficiently utilize the computing resources and provide good quality of service (QoS) to the
end-to-end tasks in the distributed real-time systems, we can enforce the utilization bounds on multiple
processors. The utilization control is challenging especially when the workload in the system is
unpredictable. To handle the workload uncertainties, current research favors feedback control techniques,
and recent work combines the task rate adaptation and processor frequency scaling in an asynchronous
way for CPU utilization control, where task rates and the processor frequencies are tuned asynchronously
in two decoupled control loops for control convenience. Since the two manipulated variables, task rates
and processor frequencies, contribute to the CPU utilizations together with strong coupling, adjusting
them asynchronously may degrade the utilization control performance. In this paper, we provide a novel
scheme to make synchronous rate and frequency adjustment to enforce the utilization setpoint, referred to
as SyRaFa scheme. SyRaFa can handle the workload uncertainties by identifying the system model online
and can simultaneously adjust the manipulated variables by solving an optimization problem in each
sampling period. Extensive evaluation results demonstrate SyRaFa outperforms the existing schemes
especially under severe workload uncertainties.
ETPL
PDS-085
Anchor: A Versatile and Efficient Framework for Resource Management in the Cloud
Abstract: We present Anchor, a general resource management architecture that uses the stable matching
framework to decouple policies from mechanisms when mapping virtual machines to physical servers. In
Anchor, clients and operators are able to express a variety of distinct resource management policies as
they deem fit, and these policies are captured as preferences in the stable matching framework. The
highlight of Anchor is a new many-to-one stable matching theory that efficiently matches VMs with
heterogeneous resource needs to servers, using both offline and online algorithms. Our theoretical
analyses show the convergence and optimality of the algorithm. Our experiments with a prototype
implementation on a 20-node server cluster, as well as large-scale simulations based on real-world
workload traces, demonstrate that the architecture is able to realize a diverse set of policy objectives with
good performance and practicality.
ETPL
PDS-086
Efficient Resource Mapping Framework over Networked Clouds via Iterated Local
Search-Based Request Partitioning
Abstract: The cloud represents a computing paradigm where shared configurable resources are provided
as a service over the Internet. Adding intra- or intercloud communication resources to the resource mix
leads to a networked cloud computing environment. Following the cloud infrastructure as a Service
paradigm and in order to create a flexible management framework, it is of paramount importance to
address efficiently the resource mapping problem within this context. To deal with the inherent
complexity and scalability issue of the resource mapping problem across different administrative

domains, in this paper a hierarchical framework is described. First, a novel request partitioning approach
based on Iterated Local Search is introduced that facilitates the cost-efficient and online splitting of user
requests among eligible cloud service providers (CPs) within a networked cloud environment. Following
and capitalizing on the outcome of the request partitioning phase, the embedding phase-where the actual
mapping of requested virtual to physical resources is performed can be realized through the use of a
distributed intracloud resource mapping approach that allows for efficient and balanced allocation of
cloud resources. Finally, a thorough evaluation of the proposed overall framework on a simulated
networked cloud environment is provided and critically compared against an exact request partitioning
solution as well as another common intradomain virtual resource embedding solution.
ETPL
PDS-087
Optimal Multiserver Configuration for Profit Maximization in Cloud Computing
Abstract: As cloud computing becomes more and more popular, understanding the economics of cloud
computing becomes critically important. To maximize the profit, a service provider should understand
both service charges and business costs, and how they are determined by the characteristics of the
applications and the configuration of a multiserver system. The problem of optimal multiserver
configuration for profit maximization in a cloud computing environment is studied. Our pricing model
takes such factors into considerations as the amount of a service, the workload of an application
environment, the configuration of a multiserver system, the service-level agreement, the satisfaction of a
consumer, the quality of a service, the penalty of a low-quality service, the cost of renting, the cost of
energy consumption, and a service provider's margin and profit. Our approach is to treat a multiserver
system as an M/M/m queuing model, such that our optimization problem can be formulated and solved
analytically. Two server speed and power consumption models are considered, namely, the idle-speed
model and the constant-speed model. The probability density function of the waiting time of a newly
arrived service request is derived. The expected service charge to a service request is calculated. The
expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server
size and the optimal server speed are demonstrated.
ETPL
PDS-088
Error-Tolerant Resource Allocation and Payment Minimization for Cloud System
Abstract: With virtual machine (VM) technology being increasingly mature, compute resources in cloud
systems can be partitioned in fine granularity and allocated on demand. We make three contributions in
this paper: 1) We formulate a deadline-driven resource allocation problem based on the cloud
environment facilitated with VM resource isolation technology, and also propose a novel solution with
polynomial time, which could minimize users' payment in terms of their expected deadlines. 2) By
analyzing the upper bound of task execution length based on the possibly inaccurate workload prediction,
we further propose an error-tolerant method to guarantee task's completion within its deadline. 3) We
validate its effectiveness over a real VM-facilitated cluster environment under different levels of
competition. In our experiment, by tuning algorithmic input deadline based on our derived bound, task
execution length can always be limited within its deadline in the sufficient-supply situation; the mean
execution length still keeps 70 percent as high as user-specified deadline under the severe competition.
Under the original-deadline-based solution, about 52.5 percent of tasks are completed within 0.95-1.0 as
high as their deadlines, which still conforms to the deadline-guaranteed requirement. Only 20 percent of
tasks violate deadlines, yet most (17.5 percent) are still finished within 1.05 times of deadlines.

ETPL
PDS-089
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing
Environment
Abstract: Cloud computing allows business customers to scale up and down their resource usage based on
needs. Many of the touted gains in the cloud model come from resource multiplexing through
virtualization technology. In this paper, we present a system that uses virtualization technology to allocate
data center resources dynamically based on application demands and support green computing by
optimizing the number of servers in use. We introduce the concept of "skewness” to measure the
unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can
combine different types of workloads nicely and improve the overall utilization of server resources. We
develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace
driven simulation and experiment results demonstrate that our algorithm achieves good performance.
ETPL
PDS-090
Performance Enhancement for Network I/O Virtualization with Efficient Interrupt
Coalescing and Virtual Receive-Side Scaling
Abstract: Virtualization is a key technology in cloud computing; it can accommodate numerous guest
VMs to provide transparent services, such as live migration, high availability, and rapid checkpointing.
Cloud computing using virtualization allows workloads to be deployed and scaled quickly through the
rapid provisioning of virtual machines on physical machines. However, I/O virtualization, particularly for
networking, suffers from significant performance degradation in the presence of high-speed networking
connections. In this paper, we first analyze performance challenges in network I/O virtualization and
identify two problems-conventional network I/O virtualization suffers from excessive virtual interrupts to
guest VMs, and the back-end driver does not efficiently use the computing resources of underlying
multicore processors. To address these challenges, we propose optimization methods for enhancing the
networking performance: 1) Efficient interrupt coalescing for network I/O virtualization and 2) virtual
receive-side scaling to effectively leverage multicore processors. These methods are implemented and
evaluated with extensive performance tests on a Xen virtualization platform. Our experimental results
confirm that the proposed optimizations can significantly improve network I/O virtualization performance
and effectively solve the performance challenges.
ETPL
PDS-091
A New Disk I/O Model of Virtualized Cloud Environment
Abstract: In a traditional virtualized cloud environment, using asynchronous I/O in the guest file system
and synchronous I/O in the host file system to handle an asynchronous user disk write exhibits several
drawbacks, such as performance disturbance among different guests and consistency maintenance across
guest failures. To improve these issues, this paper introduces a novel disk I/O model for virtualized cloud
system called HypeGear, where the guest file system uses synchronous operations to deal with the guest
write request and the host file system performs asynchronous operations to write the data to the hard disk.
A prototype system is implemented on the Xen hypervisor and our experimental results verify that this
new model has many advantages over the conventional asynchronous-synchronous model. We also
evaluate the overhead of asynchronous I/O at host, which is brought by our new model. The result
demonstrates that it enforces little cost on host layer.
ETPL
PDS-092
Improving Data Center Network Utilization Using Near-Optimal Traffic Engineering

Abstract: Equal cost multiple path (ECMP) forwarding is the most prevalent multipath routing used in
data center (DC) networks today. However, it fails to exploit increased path diversity that can be provided
by traffic engineering techniques through the assignment of nonuniform link weights to optimize network
resource usage. To this extent, constructing a routing algorithm that provides path diversity over
nonuniform link weights (i.e., unequal cost links), simplicity in path discovery and optimality in
minimizing maximum link utilization (MLU) is nontrivial. In this paper, we have implemented and
evaluated the Penalizing Exponential Flow-spliTing (PEFT) algorithm in a cloud DC environment based
on two dominant topologies, canonical and fat tree. In addition, we have proposed a new cloud DC
topology which, with only a marginal modification of the current canonical tree DC architecture, can
further reduce MLU and increase overall network capacity utilization through PEFT routing.
ETPL
PDS-093
Electricity Cost Saving Strategy in Data Centers by Using Energy Storage
Abstract: Electricity expenditure comprises a significant fraction of the total operating cost in data
centers. Hence, cloud service providers are required to reduce electricity cost as much as possible. In this
paper, we consider utilizing existing energy storage capabilities in data centers to reduce electricity cost
under wholesale electricity markets, where the electricity price exhibits both temporal and spatial
variations. A stochastic program is formulated by integrating the center-level load balancing, the server-
level configuration, and the battery management while at the same time guaranteeing the quality-of-
service experience by end users. We use the Lyapunov optimization technique to design an online
algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. We
demonstrate the effectiveness of our proposed algorithm through extensive numerical evaluations based
on real-world workload and electricity price data sets. As far as we know, our work is the first to explore
the problem of electricity cost saving using energy storage in multiple data centers by considering both
the spatial and temporal variations in wholesale electricity prices and workload arrival processes.
ETPL
PDS-094
Simple and Effective Dynamic Provisioning for Power-Proportional Data Centers
Abstract: Energy consumption represents a significant cost in data center operation. A large fraction of
the energy, however, is used to power idle servers when the workload is low. Dynamic provisioning
techniques aim at saving this portion of the energy, by turning off unnecessary servers. In this paper, we
explore how much gain knowing future workload information can bring to dynamic provisioning. In
particular, we develop online dynamic provisioning solutions with and without future workload
information available. We first reveal an elegant structure of the offline dynamic provisioning problem,
which allows us to characterize the optimal solution in a “divide-andconquer” manner. We then exploit
this insight to design two online algorithms with competitive ratios 2 - α and e/(e - 1 + α), respectively,
where 0 ≤ α ≤ 1 is the normalized size of a look-ahead window in which future workload information is
available. A fundamental observation is that future workload information beyond the full-size look-ahead
window (corresponding to α = 1) will not improve dynamic provisioning performance. Our algorithms are
decentralized and easy to implement. We demonstrate their effectiveness in simulations using real-world
traces.

ETPL
PDS-095
Harnessing the Cloud for Securely Outsourcing Large-Scale Systems of Linear
Equations
Abstract: Cloud computing economically enables customers with limited computational resources to
outsource large-scale computations to the cloud. However, how to protect customers' confidential data
involved in the computations then becomes a major security concern. In this paper, we present a secure
outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud. Because
applying traditional approaches like Gaussian elimination or LU decomposition (aka. direct method) to
such large-scale LEs would be prohibitively expensive, we build the secure LE outsourcing mechanism
via a completely different approach-iterative method, which is much easier to implement in practice and
only demands relatively simpler matrix-vector operations. Specifically, our mechanism enables a
customer to securely harness the cloud for iteratively finding successive approximations to the LE
solution, while keeping both the sensitive input and output of the computation private. For robust cheating
detection, we further explore the algebraic property of matrix-vector operations and propose an efficient
result verification mechanism, which allows the customer to verify all answers received from previous
iterative approximations in one batch with high probability. Thorough security analysis and prototype
experiments on Amazon EC2 demonstrate the validity and practicality of our proposed design.
ETPL
PDS-096
Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud
Abstract: With the character of low maintenance, cloud computing provides an economical and efficient
solution for sharing group resource among cloud users. Unfortunately, sharing data in a multi-owner
manner while preserving data and identity privacy from an untrusted cloud is still a challenging issue, due
to the frequent change of the membership. In this paper, we propose a secure multi-owner data sharing
scheme, named Mona, for dynamic groups in the cloud. By leveraging group signature and dynamic
broadcast encryption techniques, any cloud user can anonymously share data with others. Meanwhile, the
storage overhead and encryption computation cost of our scheme are independent with the number of
revoked users. In addition, we analyze the security of our scheme with rigorous proofs, and demonstrate
the efficiency of our scheme in experiments.
ETPL
PDS-097
A Privacy Leakage Upper Bound Constraint-Based Approach for Cost-Effective
Privacy Preserving of Intermediate Data Sets in Cloud
Abstract: Cloud computing provides massive computation power and storage capacity which enable users
to deploy computation and data-intensive applications without infrastructure investment. Along the
processing of such applications, a large volume of intermediate data sets will be generated, and often
stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets
becomes a challenging problem because adversaries may recover privacy-sensitive information by
analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing
approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither
efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to
en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel
upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to
be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy
requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-
preserving cost of intermediate data sets can be significantly reduced with our approach over existing

ones where all data sets are encrypted.
ETPL
PDS-098
A Truthful Dynamic Workflow Scheduling Mechanism for Commercial Multicloud
Environments
Abstract: The ultimate goal of cloud providers by providing resources is increasing their revenues. This
goal leads to a selfish behavior that negatively affects the users of a commercial multicloud environment.
In this paper, we introduce a pricing model and a truthful mechanism for scheduling single tasks
considering two objectives: monetary cost and completion time. With respect to the social cost of the
mechanism, i.e., minimizing the completion time and monetary cost, we extend the mechanism for
dynamic scheduling of scientific workflows. We theoretically analyze the truthfulness and the efficiency
of the mechanism and present extensive experimental results showing significant impact of the selfish
behavior of the cloud providers on the efficiency of the whole system. The experiments conducted using
real-world and synthetic workflow applications demonstrate that our solutions dominate in most cases the
Pareto-optimal solutions estimated by two classical multiobjective evolutionary algorithms.
ETPL
PDS-099
QoS Ranking Prediction for Cloud Services
Abstract: Cloud computing is becoming popular. Building high-quality cloud applications is a critical
research problem. QoS rankings provide valuable information for making optimal cloud service selection
from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on
the service candidates are usually required. To avoid the time-consuming and expensive real-world
service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking
advantage of the past service usage experiences of other consumers. Our proposed framework requires no
additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS
ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive
experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-
world web services all over the world. The experimental results show that our approaches outperform
other competing approaches.
ETPL
PDS-100
Cloudy with a Chance of Cost Savings
Abstract: Cloud-based hosting is claimed to possess many advantages over traditional in-house (on-
premise) hosting such as better scalability, ease of management, and cost savings. It is not difficult to
understand how cloud-based hosting can be used to address some of the existing limitations and extend
the capabilities of many types of applications. However, one of the most important questions is whether
cloud-based hosting will be economically feasible for my application if migrated into the cloud. It is not
straightforward to answer this question because it is not clear how my application will benefit from the
claimed advantages, and, in turn, be able to convert them into tangible cost savings. Within cloud-based
hosting offerings, there is a wide range of hosting options one can choose from, each impacting the cost in
a different way. Answering these questions requires an in-depth understanding of the cost implications of
all the possible choices specific to my circumstances. In this study, we identify a diverse set of key factors
affecting the costs of deployment choices. Using benchmarks representing two different applications
(TPC-W and TPC-E) we investigate the evolution of costs for different deployment choices. We consider

important application characteristics such as workload intensity, growth rate, traffic size, storage, and
software license to understand their impact on the overall costs. We also discuss the impact of workload
variance and cloud elasticity, and certain cost factors that are subjective in nature.
ETPL
PDS-101
A Highly Practical Approach toward Achieving Minimum Data Sets Storage Cost in
the Cloud
Abstract: Massive computation power and storage capacity of cloud computing systems allow scientists
to deploy computation and data intensive applications without infrastructure investment, where large
application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and
benchmarking approaches have been developed for cost-effectively storing large volume of generated
application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or
impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we
propose a novel highly cost-effective and practical storage strategy that can automatically decide whether
a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the
local-optimization for the tradeoff between computation and storage, while secondarily also taking users'
(optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted
on general (random) data sets as well as specific real world applications with Amazon's cost model show
that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark,
and the efficiency is very high for practical runtime utilization in the cloud.
ETPL
PDS-102
Toward Fine-Grained, Unsupervised, Scalable Performance Diagnosis for Production
Cloud Computing Systems
Abstract: Performance diagnosis is labor intensive in production cloud computing systems. Such systems
typically face many real-world challenges, which the existing diagnosis techniques for such distributed
systems cannot effectively solve. An efficient, unsupervised diagnosis tool for locating fine-grained
performance anomalies is still lacking in production cloud computing systems. This paper proposes
CloudDiag to bridge this gap. Combining a statistical technique and a fast matrix recovery algorithm,
CloudDiag can efficiently pinpoint fine-grained causes of the performance problems, which does not
require any domain-specific knowledge to the target system. CloudDiag has been applied in a practical
production cloud computing systems to diagnose performance problems. We demonstrate the
effectiveness of CloudDiag in three real-world case studies.
ETPL
PDS-103
A Fast RPC System for Virtual Machines
Abstract: Despite the advances in high performance interdomain communications for virtual machines
(VM), data intensive applications developed for VMs based on the traditional remote procedure call
(RPC) mechanism still suffer from performance degradation due to the inherent inefficiency of data
serialization/deserilization operations. This paper presents VMRPC, a lightweight RPC framework
specifically designed for VMs that leverages the heap and stack sharing mechanism to circumvent
unnecessary data copy and serialization/deserilization. Our evaluation shows that the performance of
VMRPC is an order of magnitude better than traditional RPC systems and existing alternative
interdomain communication optimization systems. The evaluation on a VMRPC-enhanced networked file
system across a varied range of benchmarks further reveals the competitiveness of VMRPC in IO-

intensive applications.
ETPL
PDS-103
ASAP: Scalable Collision Arbitration for Large RFID Systems
Abstract: The growing importance of operations such as identification, location sensing, and object
tracking has led to increasing interests in contactless Radio Frequency Identification (RFID) systems.
Enjoying the low cost of RFID tags, modern RFID systems tend to be deployed for large-scale mobile
objects. Both the theoretical and experimental results suggest that when tags are in large numbers, most
existing collision arbitration protocols do not satisfy the scalability and time-efficiency requirements of
many applications. To address this problem, we propose Adaptively Splitting-based Arbitration Protocol
(ASAP), a scheme that provides efficient RFID identification for both small and large deployment of
RFID tags, in terms of time and energy cost. Theoretical analysis and simulation evaluation show that the
performance of ASAP is better than most existing collision-arbitration solutions and the time efficiency is
close to the theoretically optimal values.
ETPL
PDS-103
Attached-RTS: Eliminating an Exposed Terminal Problem in Wireless Networks
Abstract: Leveraging concurrent transmission is a promising way to improve throughput in wireless
networks. Existing media access control (MAC) protocols like carrier sense multiple access always try to
minimize the number of concurrent transmissions to avoid collision, although collisions at sender sides
are harmless to the overall performance. The reason for such conservative strategy is that those protocols
cannot obtain accurate channel status (who is transmitting and receiving) with low cost. They can only
avoid potential collisions through rough channel status (idle or busy). To obtain additional information in
a cost-efficient way, we propose a novel coding scheme, Attachment Coding, to allow control information
to be “attached” on data packet. Nodes then transmit two kinds of signals simultaneously, without
degrading the effective throughput of the original data traffic. Based on Attachment Coding, we propose
an Attached-RTS MAC (AR-MAC) to exploit exposed terminals for concurrent transmissions. The
attached control information provides accurate channel status for nodes in real time. Therefore, nodes can
identify exposed terminals and utilize them for concurrent transmission. We theoretically analyze the
feasibility of Attachment Coding, and implement it on the GNU Radio testbed to further verify it. We also
conduct extensive simulations to evaluate the performance of Attached-RTS. The experimental results
show that by leveraging Attachment Coding, AR-MAC achieves up to 180 percent in dense deployed ad
hoc networks.
ETPL
PDS-103
"ESWC: Efficient Scheduling for the Mobile Sink in Wireless Sensor Networks with
Delay Constraint
Abstract: This paper exploits sink mobility to prolong the network lifetime in wireless sensor networks
where the information delay caused by moving the sink should be bounded. Due to the combinational
complexity of this problem, most previous proposals focus on heuristics and provable optimal algorithms
remain unknown. In this paper, we build a unified framework for analyzing this joint sink mobility,
routing, delay, and so on. We discuss the induced subproblems and present efficient solutions for them.
Then, we generalize these solutions and propose a polynomial-time optimal algorithm for the origin
problem. In simulations, we show the benefits of involving a mobile sink and the impact of network

parameters (e.g., the number of sensors, the delay bound, etc.) on the network lifetime. Furthermore, we
study the effects of different trajectories of the sink and provide important insights for designing mobility
schemes in real-world mobile WNNs.
ETPL
PDS-103
Grouping-Proofs-Based Authentication Protocol for Distributed RFID Systems
Abstract: Along with radio frequency identification (RFID) becoming ubiquitous, security issues have
attracted extensive attentions. Most studies focus on the single-reader and single-tag case to provide
security protection, which leads to certain limitations for diverse applications. This paper proposes a
grouping-proofs-based authentication protocol (GUPA) to address the security issue for multiple readers
and tags simultaneous identification in distributed RFID systems. In GUPA, distributed authentication
mode with independent subgrouping proofs is adopted to enhance hierarchical protection; an asymmetric
denial scheme is applied to grant fault-tolerance capabilities against an illegal reader or tag; and a
sequence-based odd-even alternation group subscript is presented to define a function for secret updating.
Meanwhile, GUPA is analyzed to be robust enough to resist major attacks such as replay, forgery,
tracking, and denial of proof. Furthermore, performance analysis shows that compared with the known
grouping-proof or yoking-proof-based protocols, GUPA has lower communication overhead and
computation load. It indicates that GUPA realizing both secure and simultaneous identification is efficient
for resource-constrained distributed RFID systems.
ETPL
PDS-103
Hint-Based Execution of Workloads in Clouds with Nefeli
Abstract: Infrastructure-as-a-Service clouds offer entire virtual infrastructures for distributed processing
while concealing all physical underlying machinery. Current cloud interface abstractions restrict users
from providing information regarding usage patterns of their requested virtual machines (VMs). In this
paper, we propose Nefeli, a virtual infrastructure gateway that lifts this restriction. Through Nefeli, cloud
consumers provide deployment hints on the possible mapping of VMs to physical nodes. Such hints
include the collocation and anticollocation of VMs, the existence of potential performance bottlenecks,
the presence of underlying hardware features (e.g., high availability), the proximity of certain VMs to
data repositories, or any other information that would contribute in a more effective placement of VMs to
physical hosting nodes. Consumers designate only properties of their virtual infrastructure and remain at
all times agnostic to the cloud internal physical characteristics. The set of consumer-provided hints is
augmented with high-level placement policies specified by the cloud administration. Placement policies
and hints form a constraint satisfaction problem that when solved, yields the final VM-to-host placement.
As workloads executed by the cloud may change over time, VM-to-host mappings must follow suit. To
this end, Nefeli captures such events, changes VM deployment, helps avoid bottlenecks, and ultimately,
improves the quality of the rendered services. Using our prototype, we examine overheads involved and
show significant improvements in terms of time needed to execute scientific and real application
workloads. We also demonstrate how power-aware policies may reduce the energy consumption of the
physical installation. Finally, we compare Nefeli's placement choices with those attained by the open-
source cloud middleware, OpenNebula.
ETPL
PDS-103
Hypocomb: Bounded-Degree Localized Geometric Planar Graphs for Wireless Ad Hoc
Networks

Abstract: We propose a radically new family of geometric graphs, i.e., Hypocomb (HC), Reduced
Hypocomb (RHC), and Local Hypocomb (LHC). HC and RHC are extracted from a complete graph;
LHC is extracted from a Unit Disk Graph (UDG). We analytically study their properties including
connectivity, planarity, and degree bound. All these graphs are connected (provided that the original
graph is connected) planar. Hypocomb has unbounded degree while Reduced Hypocomb and Local
Hypocomb have maximum degree 6 and 8, respectively. To our knowledge, Local Hypocomb is the first
strictly localized, degree-bounded planar graph computed using merely 1-hop neighbor position
information. We present a construction algorithm for these graphs and analyze its time complexity.
Hypocomb family graphs are promising for wireless ad hoc networking. We report our numerical results
on their average degree and their impact on FACE routing. We discuss their potential applications and
pinpoint some interesting open problems for future research.
ETPL
PDS-103
Link Scheduling for Exploiting Spatial Reuse in Multihop MIMO Networks
Abstract: Multiple-Input-Multiple-Output (MIMO) has great potential for enhancing the throughput of
multihop wireless networks via spatial multiplexing or spatial reuse. Spatial reuse with Stream Control
(SC) provides a considerable improvement of the network throughput over spatial multiplexing. The gain
of spatial reuse, however, is still not fully exploited. There exist large numbers of additional data streams,
which could be transmitted concurrently with those data streams scheduled by stream control at certain
time slots and vicinities. In this paper, we address the issue of MIMO link scheduling to maximize the
gain of spatial reuse and thus network throughput. We propose a Receiver-Oriented Interference
Suppression model (ROIS), based on which we design both centralized and distributed link scheduling
algorithms to fully exploit the gain of spatial reuse in multihop MIMO networks. Further, we address the
traffic-aware link scheduling problem by injecting nonuniform traffic load into the network. Through
theoretical analysis and comprehensive performance evaluation, we achieve the following results: 1) link
scheduling based on ROIS achieves significant higher network throughput than that based on stream
control, with any interference range, number of antennas, and average hop length of data flows. 2) The
traffic-aware scheduling is enticingly complementary to the link scheduling based on ROIS model.
Accordingly, the two scheduling schemes can be combined to further enhance the network throughput.
ETPL
PDS-103
Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud
Data Centers under Quality of Service Constraints
Abstract: Dynamic consolidation of virtual machines (VMs) is an effective way to improve the utilization
of resources and energy efficiency in cloud data centers. Determining when it is best to reallocate VMs
from an overloaded host is an aspect of dynamic VM consolidation that directly influences the resource
utilization and quality of service (QoS) delivered by the system. The influence on the QoS is explained by
the fact that server overloads cause resource shortages and performance degradation of applications.
Current solutions to the problem of host overload detection are generally heuristic based, or rely on
statistical analysis of historical data. The limitations of these approaches are that they lead to suboptimal
results and do not allow explicit specification of a QoS goal. We propose a novel approach that for any
known stationary workload and a given state configuration optimally solves the problem of host overload
detection by maximizing the mean intermigration time under the specified QoS goal based on a Markov
chain model. We heuristically adapt the algorithm to handle unknown nonstationary workloads using the
Multisize Sliding Window workload estimation technique. Through simulations with workload traces

from more than a thousand PlanetLab VMs, we show that our approach outperforms the best benchmark
algorithm and provides approximately 88 percent of the performance of the optimal offline algorithm.
ETPL
PDS-103
Per-Flow Queue Management with Succinct Priority Indexing Structures for High
Speed Packet Scheduling
Abstract: Priority queues are essential building blocks for implementing advanced per-flow service
disciplines and hierarchical quality-of-service at high-speed network links. Scalable priority queue
implementation requires solutions to two fundamental problems. The first is to sort queue elements in real
time at ever increasing line speeds (e.g., at OC-768 rates). The second is to store a huge number of
packets (e.g., millions of packets). In this paper, we propose novel solutions by decomposing the problem
into two parts, a succinct priority index (PI) in SRAM that can efficiently maintain a real-time sorting of
priorities, coupled with a DRAM-based implementation of large packet buffers. In particular, we propose
three related novel succinct PI data structures for implementing high-speed PIs: a PI, a counting priority
index (CPI), and a pipelined counting priority index (pCPI). We show that all three structures can be very
compactly implemented in SRAM using only ⊖(U) space, where U is the size of the universe required to
implement the priority keys (time stamps). We also show that our proposed PI structures can be
implemented very efficiently as well by leveraging hardware-optimized instructions that are readily
available in modern 64-bit processors. The operations on the PI and CPI structures take ⊖(logW U) time
complexity, where W is the processor word length (i.e., W = 64). Alternatively, operations on the pCPI
structure take amortized constant time with only ⊖(logW U) pipeline stages (e.g., only four pipeline
stages for U = 16 million). Finally, we show the application of our proposed PI structures for the scalable
management of large packet buffers at line speeds. The pCPI structure can be implemented efficiently in
high-performance network processing applications such as advanced per-flow scheduling with quality-of-
service guarantee.
ETPL
PDS-103
POVA: Traffic Light Sensing with Probe Vehicles
Abstract: Traffic light sensing aims to detect the status of traffic lights which is valuable for many
applications such as traffic management, traffic light optimization, and real-time vehicle navigation. In
this work, we develop a system called POVA for traffic light sensing in large-scale urban areas. The
system employs pervasive probe vehicles that just report real-time states of position and speed from time
to time. POVA has advantages of wide coverage and low deployment cost. The important observation
motivating the design of POVA is that a traffic light has a considerable impact on mobility of vehicles on
the road attached to the traffic light. However, the system design faces three unique challenges: 1) Probe
reports are by nature discrete while the goal of traffic light sensing is to determine the state of a traffic
light at any time; 2) there may be a very limited number of probe reports in a given duration for traffic
light state estimation; and 3) a traffic light may change its state with a variable interval. To tackle the
challenges, we develop a new technique that makes the best use of limited probe reports as well as
statistical features of light states. It first estimates the state of a traffic light at the time instant of a report
by applying maximum a posterior estimation. Then, we formulate the state estimation of a light at any
time into a joint optimization problem that is solved by an efficient heuristic algorithm. We have
implemented the system and tested it with a fleet of around 4,000 probe taxis and 2,000 buses in
Shanghai, China. Trace-driven experimentation and field study show that nearly 60 percent of traffic
lights have an estimation error lower than 19 percent if 20,000 probe vehicles would be employed in the
urban area of Shanghai. We further demonstrate that the estimation error rate is as low as 18 percent even

when the number of available reports is merely 1 per minute.
ETPL
PDS-103
Resisting Web Proxy-Based HTTP Attacks by Temporal and Spatial Locality Behavior
Abstract: A novel server-side defense scheme is proposed to resist the Web proxy-based distributed denial
of service attack. The approach utilizes the temporal and spatial locality to extract the behavior features of
the proxy-to-server traffic, which makes the scheme independent of the traffic intensity and frequently
varying Web contents. A nonlinear mapping function is introduced to protect weak signals from the
interference of infrequent large values. Then, a new hidden semi-Markov model parameterized by
Gaussian-mixture and Gamma distributions is proposed to describe the time-varying traffic behavior of
Web proxies. The new method reduces the number of parameters to be estimated, and can characterize the
dynamic evolution of the proxy-to-server traffic rather than the static statistics. Two diagnosis approaches
at different scales are introduced to meet the requirement of both fine-grained and coarse-grained
detection. Soft control is a novel attack response method proposed in this work. It converts a suspicious
traffic into a relatively normal one by behavior reshaping rather than rudely discarding. This measure can
protect the quality of services of legitimate users. The experiments confirm the effectiveness of the
proposed scheme
ETPL
PDS-103
Runtime Contention and Bandwidth-Aware Adaptive Routing Selection Strategies for
Networks-on-Chip
Abstract: This paper presents adaptive routing selection strategies suitable for network-on-chip (NoC).
The main prototype presented in this paper uses contention information and bandwidth space occupancy
to make routing decision at runtime during application execution time. The performance of the NoC
router is compared to other NoC routers with queue-length-oriented adaptive routing selection strategies.
The evaluation results show that the contention- and bandwidth-aware adaptive routing selection
strategies are better than the queue-length-oriented adaptive selection strategies. Messages in the NoC are
switched with a wormhole cut-through switching method, where different messages can be interleaved at
flit-level in the same communication link without using virtual channels. Hence, the head-of-line blocking
problem can be solved effectively and efficiently. The routing control concept and the VLSI
microarchitecture of the NoC routers are also presented in this paper.
ETPL
PDS-103
Self-Adaptive Contention Aware Routing Protocol for Intermittently Connected
Mobile Networks
Abstract: This paper introduces a novel multicopy routing protocol, called Self-Adaptive Utility-based
Routing Protocol (SAURP), for Delay Tolerant Networks (DTNs) that are possibly composed of a vast
number of devices in miniature such as smart phones of heterogeneous capacities in terms of energy
resources and buffer spaces. SAURP is characterized by the ability of identifying potential opportunities
for forwarding messages to their destinations via a novel utility function-based mechanism, in which a
suite of environment parameters, such as wireless channel condition, nodal buffer occupancy, and
encounter statistics, are jointly considered. Thus, SAURP can reroute messages around nodes

experiencing high-buffer occupancy, wireless interference, and/or congestion, while taking a considerably
small number of transmissions. The developed utility function in SAURP is proved to be able to achieve
optimal performance, which is further analyzed via a stochastic modeling approach. Extensive
simulations are conducted to verify the developed analytical model and compare the proposed SAURP
with a number of recently reported encounter-based routing approaches in terms of delivery ratio,
delivery delay, and the number of transmissions required for each message delivery. The simulation
results show that SAURP outperforms all the counterpart multicopy encounter-based routing protocols
considered in the study.
ETPL
PDS-103
Sensor Network Navigation without Locations
Abstract: We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system
for navigating internal users in locations of potential danger. Our proposed application differs from
previous work in that they typically treat the sensor network as a media of data acquisition while in our
navigation application, in-situ interactions between users and sensors become ubiquitous. In addition,
human safety and time factors are critical to the success of our objective. Without any preknowledge of
user and sensor locations, the design of an effective and efficient navigation protocol faces nontrivial
challenges. We propose to embed a road map system in the sensor network without location information
so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map
updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this
navigation system, each user only issues local queries to obtain their navigation route. The system is
highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36
TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-
scale simulations to examine the efficiency and scalability of the proposed approach under various
environmental dynamics.
ETPL
PDS-103
The Bodyguard Allocation Problem
Abstract: In this paper, we introduce the Bodyguard Allocation Problem (BAP) game, that illustrates the
behavior of processes with contradictory individual goals in distributed systems. In particular, the game
deals with the conflict of interest between two classes of processes that maximize/minimize their distance
to a special process called the root. A solution of the BAP game represents a rooted spanning tree in
which there exists a condition of equilibrium with maximum social welfare. We analyze the inefficiency
of equilibria of the game based on both a completely cooperative and noncooperative approach.
Additionally, we design two algorithms, CBAP and DBAP, that provide approximated solutions for the
BAP game. We prove that both algorithms always terminate in a configuration with equilibrium and we
analyze their running time based on the approach of cooperation used. We perform experimental
simulations to compare the overall quality of equilibria obtained by the proposed algorithms.
ETPL
PDS-103
A 3.42-Approximation Algorithm for Scheduling Malleable Tasks under Precedence
Constraints
Abstract: Scheduling malleable tasks under general precedence constraints involves finding a minimum

makespan (maximum completion time) by a feasible allotment. Based on the monotonous penalty
assumptions of Blayo et al. [2], this work defines two assumptions concerning malleable tasks: the
processing time of a malleable task is nonincreasing in the number of processors, while the work of a
malleable task is nondecreasing in the number of processors. Additionally, the work function is assumed
herein to be convex in the processing time. The proposed algorithm reformulates the linear program of
[11], and this algorithm and associated proofs are inspired by the ones of [11]. This work describes a
novel polynomial-time approximation algorithm that is capable of achieving an approximation ratio of
2+√2≈3.4142. This work further demonstrates that the proposed algorithm can yield an approximation
ratio of 2.9549 when the processing time is strictly decreasing in the number of the processors allocated to
the task. This finding represents an improvement upon the previous best approximation ratio of
100/63+100(√6469+137)/5481≈3.2920 [12] achieved under the same assumptions.
ETPL
PDS-103
Aging-Aware Energy-Efficient Workload Allocation for Mobile Multimedia Platforms
Abstract: Multicore platforms are characterized by increasing variability and aging effects that imply
heterogeneity in core performance, energy consumption, and reliability. In particular, wear-out effects
such as negative-bias-temperature-instability require runtime adaptation of system resource utilization to
time-varying and uneven platform degradation, so as to prevent premature chip failure. In this context,
task allocation techniques can be used to deal with heterogeneous cores and extend chip lifetime while
minimizing energy and preserving quality of service. We propose a new formulation of the task allocation
problem for variability affected platforms, which manages per-core utilization to achieve a target lifetime
while minimizing energy consumption during the execution of rate-constrained multimedia applications.
We devise an adaptive solution that can be applied online and approximates the result of an optimal,
offline version. Our allocator has been implemented and tested on real-life functional workloads running
on a timing accurate simulator of a next-generation industrial multicore platform. We extensively assess
the effectiveness of the online strategy both against the optimal solution and also compared to alternative
state-of-the-art policies. The proposed policy outperforms state-of-the-art strategies in terms of lifetime
preservation, while saving up to 20 percent of energy consumption without impacting timing constraints.
ETPL
PDS-103
An Efficient Penalty-Aware Cache to Improve the Performance of Parity-Based Disk
Arrays under Faulty Conditions
Abstract: The buffer cache plays an essential role in smoothing the gap between the upper level
computational components and the lower level storage devices. A good buffer cache management scheme
should be beneficial to not only the computational components, but also the storage components by
reducing disk I/Os. Existing cache replacement algorithms are well optimized for disks in normal mode,
but inefficient under faulty scenarios, such as a parity-based disk array with faulty disk(s). To address this
issue, we propose a novel penalty-aware buffer cache replacement strategy, named Victim Disk(s) First
(VDF) cache, to improve the reliability and performance of a storage system consisting of a buffer cache
and disk arrays. VDF cache gives higher priority to cache the blocks on the faulty disks when the disk
array fails, thus reducing the I/Os addressed directly to the faulty disks. To verify the effectiveness of the
VDF cache, we have integrated VDF into the popular cache algorithms least frequently used (LFU) and
least recently used (LRU), named VDF-LFU and VDF-LRU, respectively. We have conducted intensive
simulations as well as a prototype implementation for disk arrays to tolerate one disk failure (RAID-5)
and two disk failures (RAID-6). The simulation results have shown that VDF-LFU can reduce disk I/Os
to surviving disks by up to 42.3 percent in RAID-5 and 50.7 percent in RAID-6, and VDF-LRU can

reduce those by up to 36.2 percent in RAID-5 and 48.9 percent in RAID-6. Our measurement results also
show that VDF-LFU can speed up the online recovery by up to 46.3 percent in RAID-5 and 47.2 percent
in RAID-6 under spare-rebuilding mode, or improve the maximum system service rate by up to 47.7
percent in RAID-5 under degraded mode without a reconstruction workload. Similarly, VDF-LRU can
speed up the online recovery by up to 34.6 percent in RAID-5 and 38.2 percent in RAID-6, or improve
the system service rate by up to 28.4 percent in RAID-5.
ETPL
PDS-103
DoMaIN: A Novel Dynamic Location Management Solution for Internet-Based
Infrastructure Wireless Mesh Networks
Abstract: Wireless mesh networks (WMNs) have been deployed in many areas. There is an increasing
demand for supporting a large number of mobile users in WMNs. As one of the key components in
mobility management support, location management serves the purpose of tracking mobile users and
locating them prior to establishing new communications. Previous dynamic location management
schemes proposed for cellular and wireless local area networks (WLANs) cannot be directly applied to
WMNs due to the existence of multihop wireless links in WMNs. Moreover, new design challenges arise
when applying location management for silently roaming mobile users in the mesh backbone.
Considering the number of wireless hops, an important factor affecting the performance of WMNs, we
propose a DoMaIN framework that can help mobile users to decide whether an intra- or intergateway
location update (LU) is needed to ensure the best location management performance (i.e., packet delivery)
among dynamic location management solutions. In addition, by dynamically guiding mobile users to
perform LU to a desirable location entity, the proposed DoMaIN framework can minimize the location
management protocol overhead in terms of LU overhead in the mesh backbone. Furthermore, DoMaIN
brings extra benefits for supporting a dynamic hop-based LU triggering method that is different from
previous dynamic LU triggering schemes proposed for cellular networks and WLANs. We evaluate the
performance of DoMaIN in different case studies using OPNET simulations. Comprehensive simulation
results demonstrate that DoMaIN outperforms other location management schemes and is a satisfactory
location management solution for a large number of mobile users silently and arbitrarily roaming under
the wireless mesh backbone.
ETPL
PDS-103
Efficient Computation of Robust Average of Compressive Sensing Data in Wireless
Sensor Networks in the Presence of Sensor Faults
Abstract: Wireless sensor networks (WSNs) enable the collection of physical measurements over a large
geographic area. It is often the case that we are interested in computing and tracking the spatial-average of
the sensor measurements over a region of the WSN. Unfortunately, the standard average operation is not
robust because it is highly susceptible to sensor faults and heterogeneous measurement noise. In this
paper, we propose a computational efficient method to compute a weighted average (which we will call
robust average) of sensor measurements, which appropriately takes sensor faults and sensor noise into
consideration. We assume that the sensors in the WSN use random projections to compress the data and
send the compressed data to the data fusion centre. Computational efficiency of our method is achieved
by having the data fusion centre work directly with the compressed data streams. The key advantage of
our proposed method is that the data fusion centre only needs to perform decompression once to compute
the robust average, thus greatly reducing the computational requirements. We apply our proposed method
to the data collected from two WSN deployments to demonstrate its efficiency and accuracy.

ETPL
PDS-103
E-SmallTalker: A Distributed Mobile System for Social Networking in Physical
Proximity
Abstract: Small talk is an important social lubricant that helps people, especially strangers, initiate
conversations and make friends with each other in physical proximity. However, due to difficulties in
quickly identifying significant topics of common interest, real-world small talk tends to be superficial.
The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we
present E-SmallTalker, a distributed mobile communications system that facilitates social networking in
physical proximity. It automatically discovers and suggests topics such as common interests for more
significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential
topics by customizing service attributes to publish non-service-related information without establishing a
connection. We propose a novel iterative Bloom filter protocol that encodes topics to fit in SDP attributes
and achieves a low false-positive rate. We have implemented the system in Java ME for ease of
deployment. Our experiments on real-world phones show that it is efficient enough at the system level to
facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-
SmallTalker is the first distributed mobile system to achieve the same purpose.
ETPL
PDS-103
Formal Specification and Runtime Detection of Dynamic Properties in Asynchronous
Pervasive Computing Environments
Abstract: Formal specification and runtime detection of contextual properties is one of the primary
approaches to enabling context awareness in pervasive computing environments. Due to the intrinsic
dynamism of the pervasive computing environment, dynamic properties, which delineate concerns of
context-aware applications on the temporal evolution of the environment state, are of great importance.
However, detection of dynamic properties is challenging, mainly due to the intrinsic asynchrony among
computing entities in the pervasive computing environment. Moreover, the detection must be conducted
at runtime in pervasive computing scenarios, which makes existing schemes do not work. To address
these challenges, we propose the property detection for asynchronous context (PDAC) framework, which
consists of three essential parts: 1) Logical time is employed to model the temporal evolution of
environment state as a lattice. The active surface of the lattice is introduced as the key notion to model the
runtime evolution of the environment state; 2) Specification of dynamic properties is viewed as a formal
language defined over the trace of environment state evolution; and 3) The SurfMaint algorithm is
proposed to achieve runtime maintenance of the active surface of the lattice, which further enables
runtime detection of dynamic properties. A case study is conducted to demonstrate how the PDAC
framework enables context awareness in asynchronous pervasive computing scenarios. The SurfMaint
algorithm is implemented and evaluated over MIPA--the open-source context-aware middleware we
developed. Performance measurements show the accuracy and cost-effectiveness of SurfMaint, even
when faced with dynamic changes in the asynchronous pervasive computing environment.
ETPL
PDS-103
GPUs as Storage System Accelerators
Abstract: Massively multicore processors, such as graphics processing units (GPUs), provide, at a
comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in
the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of
system components, triggers the opportunity to redesign systems and to explore new ways to engineer
them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing

GPUs' computational power to improve the performance, reliability, or security of distributed storage
systems. In this context, we present the design of a storage system prototype that uses GPU offloading to
accelerate a number of computationally intensive primitives based on hashing, and introduce techniques
to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype
under two configurations: as a content addressable storage system that facilitates online similarity
detection between successive versions of the same file and as a traditional system that uses hashing to
preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing
applications' performance. Our results show that this technique can bring tangible performance gains
without negatively impacting the performance of concurrently running applications.
ETPL
PDS-103
High-Accuracy TDOA-Based Localization without Time Synchronization
Abstract: Localization is of great importance in mobile and wireless network applications. Time
Difference of Arrival (TDOA) is one of the widely used localization schemes, in which the target (source)
emits a signal and a number of anchors (receivers) record the arriving time of the source signal. By
calculating the time difference of different receivers, the location of the target is estimated. In such a
scheme, receivers must be precisely time synchronized. But time synchronization adds computational
cost, and brings errors which may lower localization accuracy. Previous studies have shown that existing
time synchronization approaches using low-cost devices are insufficiently accurate, or even infeasible
under high requirement for accuracy. In our scheme (called Whistle), several asynchronous receivers
record a target signal and a successive signal that is generated artificially. By two-signal sensing and
sample counting techniques, time synchronization requirement can be removed, while high time
resolution can be achieved. This design fundamentally changes TDOA in the sense of releasing the
synchronization requirement and avoiding many sources of errors caused by time synchronization. We
implement Whistle on commercial off-the-shelf (COTS) cell phones with acoustic signal and perform
simulations with UWB signal. Especially we use Whistle to localize nodes of large-scale wireless
networks, and also achieve desirable results. The extensive real-world experiments and simulations show
that Whistle can be widely used with good accuracy.
ETPL
PDS-103
Intelligent Sensor Placement for Hot Server Detection in Data Centers
Abstract: Recent studies have shown that a significant portion of the total energy consumption of many
data centers is caused by the inefficient operation of their cooling systems. Without effective thermal
monitoring with accurate location information, the cooling systems often use unnecessarily low
temperature set points to overcool the entire room, resulting in excessive energy consumption. Sensor
network technology has recently been adopted for data-center thermal monitoring because of its
nonintrusive nature for the already complex data center facilities and robustness to instantaneous CPU or
disk activities. However, existing solutions place sensors in a simplistic way without considering the
thermal dynamics in data centers, resulting in unnecessarily degraded hot server detection probability. In
this paper, we first formulate the problems of sensor placement for hot server detection in a data center as
constrained optimization problems in two different scenarios. We then propose a novel placement scheme
based on computational fluid dynamics (CFD) to take various factors, such as cooling systems and server
layout, as inputs to analyze the thermal conditions of the data center. Based on the CFD analysis in

various server overheating scenarios, we apply data fusion and advanced optimization techniques to find a
near-optimal sensor placement solution, such that the probability of detecting hot servers is significantly
improved. Our empirical results in a real server room demonstrate the detection performance of our
placement solution. Extensive simulation results in a large-scale data center with 32 racks also show that
the proposed solution outperforms several commonly used placement solutions in terms of detection
probability.
ETPL
PDS-103
ITA: Innocuous Topology Awareness for Unstructured P2P Networks
Abstract: One of the most appealing characteristics of unstructured P2P overlays is their enhanced self-*
properties, which results from their loose, random structure. In addition, most of the algorithms which
make searching in unstructured P2P systems scalable, such as dynamic querying and 1-hop replication,
rely on the random nature of the overlay to function efficiently. The underlying communications network
(i.e., the Internet), however, is not as randomly constructed. This leads to a mismatch between the
distance of two peers on the overlay and the hosts they reside on at the IP layer, which in turn leads to its
misuse. The crux of the problem arises from the fact that any effort to provide a better match between the
overlay and the IP layer will inevitably lead to a reduction in the random structure of the P2P overlay,
with many adverse results. With this in mind, we propose ITA, an algorithm which creates a random
overlay of randomly connected neighborhoods providing topology awareness to P2P systems, while at the
same time has no negative effect on the self-* properties or the operation of the other P2P algorithms.
Using extensive simulations, both at the IP router level and autonomous system level, we show that ITA
reduces communication latencies by as much as 50 percent. Furthermore, it not only reduces by 20
percent the number of IP network messages which is critical for ISPs carrying the burden of transporting
P2P traffic, but also distributes the traffic load more evenly on the routers of the IP network layer.
ETPL
PDS-103
K-Means for Parallel Architectures Using All-Prefix-Sum Sorting and Updating Steps
Abstract: We present an implementation of parallel $(K)$-means clustering, called $(K_{ps})$-means,
that achieves high performance with near-full occupancy compute kernels without imposing limits on the
number of dimensions and data points permitted as input, thus combining flexibility with high degrees of
parallelism and efficiency. As a key element to performance improvement, we introduce parallel sorting
as data preprocessing and updating steps. Our final implementation for Nvidia GPUs achieves speedups
of up to 200-fold over CPU reference code and of up to three orders of magnitude when compared with
popular numerical software packages.
ETPL
PDS-103
LU Factorization with Partial Pivoting for a Multicore System with Accelerators
Abstract: LU factorization with partial pivoting is a canonical numerical procedure and the main
component of the high performance LINPACK benchmark. This paper presents an implementation of the
algorithm for a hybrid, shared memory, system with standard CPU cores and GPU accelerators. The
difficulty of implementing the algorithm for such a system lies in the disproportion between the
computational power of the CPUs, compared to the GPUs, and in the meager bandwidth of the

communication link between their memory systems. An additional challenge comes from the complexity
of the memory-bound and synchronization-rich nature of the panel factorization component of the block
LU algorithm, imposed by the use of partial pivoting. The challenges are tackled with the use of a data
layout geared toward complex memory hierarchies, autotuning of GPU kernels, fine-grain parallelization
of memory-bound CPU operations and dynamic scheduling of tasks to different devices. Performance in
excess of one TeraFLOPS is achieved using four AMD Magny Cours CPUs and four NVIDIA Fermi
GPUs.
ETPL
PDS-103
LvtPPP: Live-Time Protected Pseudopartitioning of Multicore Shared Caches
Abstract: Partition enforcement policy is essential in the cache partition, and its main function is to
protect the lines and retain the cache quota of each core. This paper focuses online protection based on its
generation time rather than the CPU core ID that it belongs to or the position of the replacement stack,
where it is located. The basic idea is that when a line is live, it must be protected and retained in the
cache; when the line is "dead," it needs to be evicted as early as possible. Therefore, the
live-time protected counter (LvtP, four bits) is augmented to trace the lines' live time. Moreover, dead
blocks are predicted according to the access event sequence. This paper presents a pseudopartition
approach--LvtPPP and proposes a two-cascade victim selection mechanism to alleviate dead blocks based
on the LRU replacement policy and the LvtP counter. LvtPPP also supports flexible handling of
allocation deviation by introducing a parameter $(lambda)$ to adjust the generation time of the line.
There is significant improvement of the performance and fairness in LvtPPP over PIPP and UCP
according to the evaluation results based on Simics.
ETPL
PDS-103
Modeling Propagation Dynamics of Social Network Worms
Abstract: Social network worms, such as email worms and facebook worms, pose a critical security threat
to the Internet. Modeling their propagation dynamics is essential to predict their potential damages and
develop countermeasures. Although several analytical models have been proposed for modeling
propagation dynamics of social network worms, there are two critical problems unsolved: temporal
dynamics and spatial dependence. First, previous models have not taken into account the different time
periods of Internet users checking emails or social messages, namely, temporal dynamics. Second, the
problem of spatial dependence results from the improper assumption that the states of neighboring nodes
are independent. These two problems seriously affect the accuracy of the previous analytical models. To
address these two problems, we propose a novel analytical model. This model implements a spatial-
temporal synchronization process, which is able to capture the temporal dynamics. Additionally, we find
the essence of spatial dependence is the spreading cycles. By eliminating the effect of these cycles, our
model overcomes the computational challenge of spatial dependence and provides a stronger
approximation to the propagation dynamics. To evaluate our susceptible-infectious-immunized (SII)
model, we conduct both theoretical analysis and extensive simulations. Compared with previous epidemic
models and the spatial-temporal model, the experimental results show our SII model achieves a greater
accuracy. We also compare our model with the susceptible-infectious-susceptible and susceptible-
infectious-recovered models. The results show that our model is more suitable for modeling the
propagation of social network worms.

ETPL
PDS-103
Online Balancing Two Independent Criteria upon Placements and Deletions
Abstract: The analysis of real-world complex networks has been the focus of recent research. Detecting
communities helps in uncovering their structural and functional organization. Valuable insight can be
obtained by analyzing the dense, overlapping, and highly interwoven $(k)$-clique communities.
However, their detection is challenging due to extensive memory requirements and execution time. In this
paper, we present a novel, parallel $(k)$-clique community detection method, based on an innovative
technique which enables connected components of a network to be obtained from those of its
subnetworks. The novel method has an unbounded, user-configurable, and input-independent maximum
degree of parallelism, and hence is able to make full use of computational resources. Theoretical tight
upper bounds on its worst case time and space complexities are given as well. Experiments on real-world
networks such as the Internet and the World Wide Web confirmed the almost optimal use of parallelism
(i.e., a linear speedup). Comparisons with other state-of-the-art $(k)$-clique community detection
methods show dramatic reductions in execution time and memory footprint. An open-source
implementation of the method is also made publicly available.
ETPL
PDS-103
Scalable Hypergrid k-NN-Based Online Anomaly Detection in Wireless Sensor
Networks
Abstract: Online anomaly detection (AD) is an important technique for monitoring wireless sensor
networks (WSNs), which protects WSNs from cyberattacks and random faults. As a scalable and
parameter-free unsupervised AD technique, $(k)$-nearest neighbor (kNN) algorithm has attracted a lot of
attention for its applications in computer networks and WSNs. However, the nature of lazy-learning
makes the kNN-based AD schemes difficult to be used in an online manner, especially when
communication cost is constrained. In this paper, a new kNN-based AD scheme based on hypergrid
intuition is proposed for WSN applications to overcome the lazy-learning problem. Through redefining
anomaly from a hypersphere detection region (DR) to a hypercube DR, the computational complexity is
reduced significantly. At the same time, an attached coefficient is used to convert a hypergrid structure
into a positive coordinate space in order to retain the redundancy for online update and tailor for bit
operation. In addition, distributed computing is taken into account, and position of the hypercube is
encoded by a few bits only using the bit operation. As a result, the new scheme is able to work
successfully in any environment without human interventions. Finally, the experiments with a real WSN
data set demonstrate that the proposed scheme is effective and robust.
ETPL
PDS-103
Task Allocation for Undependable Multiagent Systems in Social Networks
Abstract: Task execution of multiagent systems in social networks (MAS-SN) can be described through
agents' operations when accessing necessary resources distributed in the social networks; thus, task
allocation can be implemented based on the agents' access to the resources required for each task and
aimed to minimize this resource access time. Currently, in undependable MAS-SN, there are deceptive
agents that may fabricate their resource status information during task allocation but not really contribute
resources to task execution; although there are some game theory-based solutions for undependable MAS,
but which do not consider minimizing resource access time that is crucial to the performance of task
execution in social networks. To achieve dependable resources with the least access time to execute tasks
in undependable MAS-SN, this paper presents a novel task allocation model based on the negotiation

reputation mechanism, where an agent's past behaviors in the resource negotiation of task execution can
influence its probability to be allocated new tasks in the future. In this model, the agent that contributes
more dependable resources with less access time during task execution is rewarded with a higher
negotiation reputation, and may receive preferential allocation of new tasks. Through experiments, we
determine that our task allocation model is superior to the traditional resources-based allocation
approaches and game theory-based allocation approaches in terms of both the task allocation success rate
and task execution time and that it usually performs close to the ideal approach (in which deceptive
agents are fully detected) in terms of task execution time.
ETPL
PDS-103
Two Blocks Are Enough: On the Feasibility of Using Network Coding to Ameliorate
the Content Availability of BitTorrent Swarms
Abstract: In this paper, we conduct an in-depth study on the feasibility of using network coding to
ameliorate the content availability of BitTorrent swarms. We first perform mathematical analysis on the
potential improvement in the content availability and bandwidth utilization induced by two existing
network coding schemes. It is found that these two coding schemes either incur a very high coding
complexity and disk operation overhead or cannot effectively leverage the potential of improving the
content availability. In this regard, we propose a simple sparse network coding scheme in which both the
drawbacks mentioned before are precluded. To accommodate the proposed coding scheme into
BitTorrent, a new block scheduling algorithm is also developed based on the original rarest-first block
scheduling policy of BitTorrent. Through extensive simulations and performance evaluations, we show
that the proposed coding scheme is very effective in terms of improving the content availability of
BitTorrent swarms when compared with some existing methods.
ETPL
PDS-103
Virtual Batching: Request Batching for Server Energy Conservation in Virtualized
Data Centers
Abstract: Many power management strategies have been proposed for enterprise servers based on
dynamic voltage and frequency scaling (DVFS), but those solutions cannot further reduce the energy
consumption of a server when the server processor is already at the lowest DVFS level and the server
utilization is still low (e.g., 10 percent or lower). To achieve improved energy efficiency, request batching
can be conducted to group received requests into batches and put the processor into sleep between the
batches. However, it is challenging to perform request batching on a virtualized server because different
virtual machines on the same server may have different workload intensities. Hence, putting the shared
processor into sleep may severely impact the application performance of all the virtual machines. This
paper proposes Virtual Batching, a novel request batching solution for virtualized servers with primarily
light workloads. Our solution dynamically allocates CPU resources such that all the virtual machines can
have approximately the same performance level relative to their allowed peak values. Based on this
uniform level, Virtual Batching determines the time length for periodically batching incoming requests
and putting the processor into sleep. When the workload intensity changes from light to moderate, request
batching is automatically switched to DVFS to increase processor frequency for performance guarantees.
Virtual Batching is also extended to integrate with server consolidation for maximized energy
conservation with performance guarantees for virtualized data centers. Empirical results based on a
hardware testbed and real trace files show that Virtual Batching can achieve the desired performance with
more energy conservation than several well-designed baselines, e.g., 63 percent more, on average, than a
solution based on DVFS only

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