IEEE Service computing 2016 Title and Abstract

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IEEE TRANSACTION ON SERVICE COMPUTING
Exploiting Spatio-Temporal Diversity for Water Saving in Geo-Distributed Data Centers
Abstract - As the critical infrastructure for supporting Internet and cloud computing services,
massive geo-distributed data centers are notorious for their huge electricity appetites and carbon
footprints. Nonetheless, a lesser-known fact is that data centers are also “thirsty”: to operate data
centers, millions of gallons of water are required for cooling and electricity production. The
existing watersaving techniques primarily focus on improved “engineering” (e.g., upgrading to
air economizer cooling, diverting recycled/sea water instead of potable water) and do not apply
to all data centers due to high upfront capital costs and/or location restrictions. In this paper, we
propose a software-based approach towards water conservation by exploiting the inherent spatio-
temporal diversity of water efficiency across geo-distributed data centers. Specifically, we
propose a batch job scheduling algorithm, called WACE (minimization of WAter, Carbon and
Electricity cost), which dynamically adjusts geographic load balancing and resource provisioning
to minimize the water consumption along with carbon emission and electricity cost while
satisfying average delay performance requirement. WACE can be implemented online without
foreseeing the far future information and yields a total cost (incorporating electricity cost, water
consumption and carbon emission) that is provably close to the optimal algorithm with
lookahead information. Finally, we validate WACE through a trace-based simulation study and
show that WACE outperforms state-of-the-art benchmarks: 25% water saving while incurring an
acceptable delay increase. We also extend WACE to joint scheduling of batch workloads and
delay-sensitive interactive workloads for further water footprint reduction in geo-distributed data
centers.
IEEE Transactions on Cloud Computing (February, 2016)
Effects of Content Popularity on the Performance of Content-Centric Opportunistic
Networking: An Analytical Approach and Applications

Abstract - Mobile users are envisioned to exploit direct communication opportunities between
their portable devices, in order to enrich the set of services they can access through cellular or
WiFi networks. Sharing contents of common interest or providing access to resources or services
between peers can enhance a mobile node's capabilities, offload the cellular network, and
disseminate information to nodes without Internet access. Interest patterns, i.e., how many nodes
are interested in each content or service (popularity), as well as how many users can provide a
content or service (availability) impact the performance and feasibility of envisioned
applications. In this paper, we establish an analytical framework to study the effects of these
factors on the delay and success probability of a content/service access request through
opportunistic communication. We also apply our framework to the mobile data offloading
problem and provide insights for the optimization of its performance. We validate our model and
results through realistic simulations, using datasets of real opportunistic networks.
IEEE/ACM Transactions on Networking (February, 2016)
Fully Expanded Tree for Property Analysis of One-Place-Unbounded Petri Nets
Abstract - This paper proposes a fully expanded tree (FET) approach for one-place-unbounded
Petri nets. The FET of a one-place-unbounded Petri net consists of all and only reachable
markings from its initial marking. Its applications to liveness and deadlock analysis for such
Petri nets are developed. The proposed method has a larger application scope than all the existing
methods for them. Several examples are provided to show its superiority over the state-of-the-art
methods.
IEEE Transactions on Systems, Man, and Cybernetics: Systems (March, 2016)
Usability Evaluations of Health Institutions Inspection Software
Abstract - Dissatisfaction with health services is increasing year by year and health appears to
be the worst area assessed by population. Technological innovation, such as the use of
information technology and communication tools and computerization of processes, can assist in
the implementation of technological resources that enable the improvement of health services. A

solution developed primarily for the Regional Council of Medicine of Santa Catarina (CRM-SC)
and then expanded to the Federal Council of Medicine (CFM) - which is currently being used by
all regional councils - is CRVirtual, a system that supports the activities undertaken by councils
to assist in the health facilities inspection process. The system allows the inspection and
monitoring of inspections carried out in hospitals, clinics, health centers among others, enabling
verify which of these establishments are infringing the laws, resolutions and decrees enforced for
good services to the population. This study aims to verify the improvement points in the system
interface so that the survey carried out by agents and inspection doctors can be done with more
quality in order to facilitate the monitoring of the inspection process and assessment notice the
establishments where irregularities were detected. Empirical methods (with the participation of
the system users) and not empirical (without the users participation) for the CRVirtual interface
evaluation were: Heuristic evaluation and Usability Testing. It was identified 27 points of
improvement in the system interface. The observed users' speeches and actions analysis, as well
as the application of usability tests, showed that the system is relatively easy to be used and to
learn through. Some difficulties faced have been identified, but users feel motivated to use the
system and recognize its usefulness as an inspections registration facilitator and when monitoring
processes, in addition to providing more flexibility to the establishments inspection proce- s.
After correcting these detected points, the system was used by agents and inspection doctors
during the year 2014. The result of these inspections was an unprecedented survey of primary
care situation in Brazil, where the 952 health facilities visited, CFM concluded, after analysis of
data obtained through the CRVirtual, that the situation is worrying in all aspects.
IEEE Latin America Transactions (March 2016)
Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks
Abstract - Self-organizing networks (SON) aim at simplifying network management (NM) and
optimizing network capital and operational expenditure through automation. Most SON
functions (SFs) are rule-based control structures, which evaluate metrics and decide actions
based on a set of rules. These rigid structures are, however, very complex to design since rules

must be derived for each SF in each possible scenario. In practice, rules only support generic
behavior, which cannot respond to the specific scenarios in each network or cell. Moreover, SON
coordination becomes very complicated with such varied control structures. In this paper, we
propose to advance SON toward cognitive cellular networks (CCN) by adding cognition that
enables the SFs to independently learn the required optimal configurations. We propose a
generalized Q-learning framework for the CCN functions and show how the framework fits to a
general SF control loop. We then apply this framework to two functions on mobility robustness
optimization (MRO) and mobility load balancing (MLB). Our results show that the MRO
function learns to optimize handover performance while the MLB function learns to distribute
instantaneous load among cells.
IEEE Transactions on Network and Service Management (March 2016)
Game User-Oriented Multimedia Transmission over Cognitive Radio Networks
Abstract - Cognitive radio (CR) is an emerging technique to improve the efficiency of spectrum
resource utilization. In CR networks, the selfish behavior of secondary users (SU) can
considerably affect the performance of primary users (PU). Accordingly, game theory, which
takes into consideration of the game players‟ selfish behavior, has been applied into the design of
CR networks. Most of the existing studies focus on the network design only from the network
perspective to improve system performance such as utility and throughput. However, the users‟
experience to the service, which cannot simply be reflected by quality of service (QoS), has been
largely ignored. The user-perceived multimedia quality and service can be different from the
actual received multimedia quality, and thus is very important to take into consider of the
network design. To better serve the network users, quality of experience (QoE) is adopted to
measure the network service from the users‟ perspective and help improve the users‟ satisfaction
to the CR network service. As CR networks requires lots of data storage and computation for
spectrum sensing, spectrum sharing and algorithm design, cloud computation comes as a handy
solution because it can provide massive storage and fast computation. In this paper, we propose
to design a user-oriented CR cloud network for multimedia applications, where the user‟s

satisfaction is reflected in the CR cloud network design. In the proposed framework, the primary
and secondary user game is formulated as Stackelberg game. Specifically, a refunding term is
defined in the users‟ utility function to effectively consider and to reflect the network users‟ QoE
requirement. Our contributions include two folds: (1) A game based CR cloud network design
for multimedia transmission is proposed, and the network user‟s QoE requirement is satisfied in
the design; (2) The existence and uniqueness of the Stackelberg Nash equili- rium is proved, and
the design is optimal. Our simulation results demonstrate the effectiveness of the game user-
oriented CR cloud network design.
IEEE Transactions on Circuits and Systems for Video Technology (May, 2016)
An Optimized Virtual Load Balanced Call Admission Controller for IMS Cloud
Computing
Abstract - Network functions virtualization provides opportunities to design, deploy, and
manage networking services. It utilizes Cloud computing virtualization services that run on high-
volume servers, switches and storage hardware to virtualize network functions. Virtualization
techniques can be used in IP Multimedia Subsystem (IMS) cloud computing to develop different
networking functions (e.g. load balancing and call admission control). IMS network signaling
happens through Session Initiation Protocol (SIP). An open issue is the control of overload that
occurs when an SIP server lacks sufficient CPU and memory resources to process all messages.
This paper proposes a virtual load balanced call admission controller (VLB-CAC) for the cloud-
hosted SIP servers. VLB-CAC determines the optimal “call admission rates” and “signaling
paths” for admitted calls along with the optimal allocation of CPU and memory resources of the
SIP servers. This optimal solution is derived through a new linear programming model. This
model requires some critical information of SIP servers as input. Further, VLB-CAC is equipped
with an autoscaler to overcome resource limitations. The proposed scheme is implemented in
SAVI (Smart Applications on Virtual Infrastructure) which serves as a virtual testbed. An
assessment of the numerical and experimental results demonstrates the efficiency of the proposed
work.

IEEE Transactions on Network and Service Management (May, 2016)
Long-Term Ship Speed Prediction for Intelligent Traffic Signaling
Abstract - Yangtze River is probably the world's busiest inland waterway. Ships need to be
guided when passing through a controlled waterway based on their long-term speed prediction.
Inaccurate ship speed prediction leads to nonoptimal traffic signaling, which may cause a
significant traffic jam. For the existing intelligent traffic signaling system, the ship speed is
assumed to be constant, which has caused many problems and issues. This paper proposes a
novel algorithm to construct an improved multilayer perceptron (MLP) network for accurate
long-term ship speed prediction, in which the hidden neurons of the MLP are optimized by the
particle swarm optimization method. The effectiveness and efficiency of the method are
guaranteed by using the orthogonal least squares method, which is the fast approach for the
construction of the MLP network in a stepwise forward procedure. The model is driven by easily
acquired dynamic data of the ships, including the speed and the position. The effectiveness of the
proposed method is further confirmed by comparing with several traditional modeling
techniques. To the best of our knowledge, this is the first time that a ship speed model is built for
long-term prediction. The experimental results show that the developed model is in good
agreement with the real-life data, with more than 97% accuracy. It will help to generate the
optimal traffic commands for Yangtze River in an intelligent traffic signaling system.
IEEE Transactions on Intelligent Transportation Systems (May 2016)
Computational experiment research on the equalization-oriented service strategy in
collaborative manufacturing
Abstract - In the framework of Industry 4.0, collaborative manufacturing across different supply
chains is one of the most important business models. In order to avoid the uneven distribution of
service requirements among service providers (i.e. non-equalization phenomenon), a lot of
service strategies with different characteristics can be taken as candidate solutions to adjust the
matching between service providers and service consumers. Based on the background, how to

identify the application conditions of various service strategies in complex environment has
become a serious challenge in the field. To solve this problem, the computational experiment-
based evaluation method is proposed in this paper, including customization of service strategy,
construction of experiment system, and experiment analysis of service strategy. In this paper,
three possible service strategies are built to deal with the non-equalization phenomenon, i.e. non-
equalization strategy, equalization strategy, collaborative equalization strategy. Experiment
results show that: collaborative equalization strategy can effectively enhance the service
utilization rate and reduce the completion time in short supply environment; equalization strategy
is the optimal one in oversupply market environment. This case study can show that the proposed
method is feasible and the result is satisfactory.
IEEE Transactions on Services Computing – (May 2016)
GreenCoMP: Energy-Aware Cooperation for Green Cellular Networks
Abstract - Switching off base stations (BSs) is an effective and efficient energy-saving solution
for green cellular networks. The previous works focus mainly on when to switch off BSs without
sacrificing the traffic demands of current active users, and then enlarge the coverage of the stay-
on cells to cover as much more users as possible. Based on this objective, both constant power
and transmission power of each BS become the major energy consumption sources. However,
the transmission powers of enlarged cells, which have not been taken into account in previous
research, are not negligible as compared to other energy consumption sources. To tackle this
problem, we observe that the transmission power of one specific BS could be reduced via
cooperation among two or more BSs, which is typically used to improve the throughput or
enhance the spectrum efficiency in wireless systems. The challenges come mainly from how to
jointly consider which BSs to switch off and how to cooperate among active-mode BSs. In this
paper, we design energy-aware cooperation strategies that ensure that our system is energy-
saving while satisfying user demands. To cope with sleep-mode BSs and perform cooperation
among active BSs, we formulate this problem as a binary integer programming problem, and
prove it is NP-hard. Based on our formulation, we derive a performance lower bound for this

problem via Lagrangian Relaxation with search enumeration. Furthermore, we propose two
heuristic algorithms accounting for the properties of energy savings and the constraints of
bandwidth resources. The simulation results show that our algorithms outperform pure power
control mechanisms that do not consider the transmission power and pure cooperation without
power control in terms of the total consumed energy. We also observe that larger cooperative
size does not imply a better strategy under different scenarios. Compared to the total consumed
energy given that all BSs are turned on, our algorithms can save up to 60%- of energy. This
demonstrates that our methods are indeed efficient energy-saving cooperation strategies for
green cellular networks.
IEEE Transactions on Mobile Computing (March 2016)
Middleware-oriented Deployment Automation for Cloud Applications
Abstract - Fully automated provisioning and deployment of applications is one of the most
essential prerequisites to make use of the benefits of Cloud computing in order to reduce the
costs for managing applications. A huge variety of approaches, tools, and providers are available
to automate the involved processes. The DevOps community, for instance, provides tooling and
reusable artifacts to implement deployment automation in an applicationoriented manner.
Platform-as-a-Service frameworks are available for the same purpose. In this work we
systematically classify and characterize available deployment approaches independently from the
underlying technology used. For motivation and evaluation purposes, we choose Web
applications with different technology stacks and analyze their specific deployment
requirements. Afterwards, we provision these applications using each of the identified types of
deployment approaches in the Cloud to perform qualitative and quantitative measurements.
Finally, we discuss the evaluation results and derive recommendations to decide which
deployment approach to use based on the deployment requirements of an application. Our results
show that deployment approaches can also be efficiently combined if there is no „best fit‟ for a
particular application.
IEEE Transactions on Cloud Computing (February 2016)

Frame Interpolation for Cloud-Based Mobile Video Streaming
Abstract - Cloud-based High Definition (HD) video streaming is becoming popular day by day.
On one hand, it is important for both end users and large storage servers to store their huge
amount of data at different locations and servers. On the other hand, it is becoming a big
challenge for network service providers to provide reliable connectivity to the network users.
There have been many studies over cloud-based video streaming for Quality of Experience
(QoE) for services like YouTube. Packet losses and bit errors are very common in transmission
networks, which affect the user feedback over cloud-based media services. To cover up packet
losses and bit errors, Error Concealment (EC) techniques are usually applied at the
decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and
quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded
videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach
is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search
process for the lost MVs, a bigger block size and searching in parallel are both considered. The
simulation results clearly show that our proposed method outperforms the traditional Block
Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a
packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The
computational time of the proposed approach outperforms the BMA by approximately 1788
seconds.
IEEE Transactions on Multimedia (May 2016)
Pattern Prediction and Passive Bandwidth Management for Hand-Over Optimization in
QoS Cellular Networks with Vehicular Mobility
Abstract - In wireless networking the main desire of end-users is to take advantage of
satisfactory services, in terms of QoS, especially when they pay for a required need. Many
efforts have been made to investigate how the continuity of services can be guaranteed in QoS
networks, where users can move from one cell to another one. The introduction of a prediction
scheme with passive reservations is the only way to face this issue; however, the deployment of

in-advance bandwidth leads the system to waste resources. This work consists of two main
integrated contributions: a new pattern prediction scheme based on a distributed set of Markov
chains, in order to handle passive reservations, and a statistical bandwidth management
algorithm for the reduction of bandwidth wastage. The result of the integration is the Distributed
Prediction with Bandwidth Management Algorithm (DPBMA) that is independent from the
considered technology and the vehicular environment. Several simulation campaigns were
conducted in order to evaluate the effectiveness of the proposed idea. It was also compared with
other prediction schemes, in terms of system utilization, accuracy, call dropping and call
blocking probabilities
IEEE Transactions on Mobile Computing (January 2016)
An Optimal Divisioning Technique to Stabilization Synthesis of T-S Fuzzy Delayed Systems
Abstract - This paper investigates the problem of stability analysis and stabilization for Takagi-
Sugeno (T-S) fuzzy systems with time-varying delay. By using appropriately chosen Lyapunov-
Krasovskii functional, together with the reciprocally convex a new sufficient stability condition
with the idea of delay partitioning approach is proposed for the delayed T-S fuzzy systems,
which significantly reduces conservatism as compared with the existing results. On the basis of
the obtained stability condition, the state-feedback fuzzy controller via parallel distributed
compensation law is developed for the resulting fuzzy delayed systems. Furthermore, the
parameters of the proposed fuzzy controller are derived in terms of linear matrix inequalities,
which can be easily obtained by the optimization techniques. Finally, three examples (one of
them is the benchmark inverted pendulum) are used to verify and illustrate the effectiveness of
the proposed technique.
IEEE Transactions on Cybernetics (April 2016)
PathGraph: A Path Centric Graph Processing System
Abstract - Large scale iterative graph computation presents an interesting systems challenge due
to two well known problems: (1) the lack of access locality and (2) the lack of storage efficiency.

This paper presents PathGraph, a system for improving iterative graph computation on graphs
with billions of edges. First, we improve the memory and disk access locality for iterative
computation algorithms on large graphs by modeling a large graph using a collection of tree-
based partitions. This enables us to use path-centric computation rather than vertexcentric or
edge-centric computation. For each tree partition, we re-label vertices using DFS in order to
preserve consistency between the order of vertex ids and vertex order in the paths. Second, a
compact storage that is optimized for iterative graph parallel computation is developed in the
PathGraph system. Concretely, we employ delta-compression and store tree-based partitions in a
DFS order. By clustering highly correlated paths together as tree based partitions, we maximize
sequential access and minimize random access on storage media. Third but not the least, our
path-centric computation model is implemented using a scatter/gather programming model. We
parallel the iterative computation at partition tree level and perform sequential local updates for
vertices in each tree partition to improve the convergence speed. To provide well balanced
workloads among parallel threads at tree partition level, we introduce the concept of multiple
stealing points based task queue to allow work stealings from multiple points in the task queue.
We evaluate the effectiveness of PathGraph by comparing with recent representative graph
processing systems such as GraphChi and X-Stream etc. Our experimental results show that our
approach outperforms the two systems on a number of graph algorithms for both in-memory and
out-of-core graphs. While our approach achieves better data balance and load balance, it also
shows better speedup than the two - ystems with the growth of threads.
IEEE Transactions on Parallel and Distributed Systems (January 2016)
RepCloud: Attesting to Cloud Service Dependency
Abstract - Security enhancements to the emerging IaaS (Infrastructure as a Service) cloud
computing systems have become the focus of much research, but little of this targets the
underlying infrastructure. Trusted Cloud systems are proposed to integrate Trusted Computing
infrastructure with cloud systems. With remote attestations, cloud customers are able to
determine the genuine behaviors of their applications‟ hosts; and therefore they establish trust to

the cloud. However, the current Trusted Clouds have difficulties in effectively attesting to the
cloud service dependency for customers‟ applications, due to the cloud‟s complexity,
heterogeneity and dynamism. In this paper, we present RepCloud, a decentralized cloud trust
management framework, inspired by the reputation systems from the research in peerto- peer
systems. With RepCloud, cloud customers are able to determine the properties of the exact nodes
that may affect the genuine functionalities of their applications, without obtaining much internal
information of the cloud. Experiments showed that besides achieving fine-grained cloud service
dependency attestation, RepCloud incurred lower trust management overhead than the existing
trusted cloud systems.
IEEE Transactions on Services Computing (May 2016)
Novel Scheduling Algorithms for Efficient Deployment of MapReduce Applications in
Heterogeneous Computing Environments
Abstract - Cloud computing has become increasingly popular model for delivering applications
hosted in large data centers as subscription oriented services. Hadoop is a popular system
supporting the MapReduce function, which plays a crucial role in cloud computing. The
resources required for executing jobs in a large data center vary according to the job type. In
Hadoop, jobs are scheduled by default on a first-come-first-served basis, which may unbalance
resource utilization. This paper proposes a job scheduler called the job allocation scheduler
(JAS), designed to balance resource utilization. For various job workloads, the JAS categorizes
jobs and then assigns tasks to a CPU-bound queue or an I/O-bound queue. However, the JAS
exhibited a locality problem, which was addressed by developing a modified JAS called the job
allocation scheduler with locality (JASL). The JASL improved the use of nodes and the
performance of Hadoop in heterogeneous computing environments. Finally, two parameters were
added to the JASL to detect inaccurate slot settings and create a dynamic job allocation scheduler
with locality (DJASL). The DJASL exhibited superior performance than did the JAS, and data
locality similar to that of the JASL.
IEEE Transactions on Cloud Computing (April 2016)

A Platform as a Service Billing Model for Cloud Computing Management Approaches
Abstract - Platform as a Service (PaaS) billing needs an effective billing strategy. In this paper
we proceeded a literature review and proposed a new billing model for a PaaS provider. Our
billing model allows charges to PaaS clients in several policies, from specific plans to fully pay-
per-use. We automated our billing model in a monitoring and management software tool. The
model and the tool were validated through a case study in a software development company. The
results indicated that our model is useful and preferable in relation to current billing policies and
can be used in PaaS management.
IEEE Latin America Transactions (Jan, 2016)
Flexible and Fine-Grained Attribute-Based Data Storage in Cloud Computing
Abstract - With the development of cloud computing, outsourcing data to cloud server attracts
lots of attentions. To guarantee the security and achieve flexibly fine-grained file access control,
attribute based encryption (ABE) was proposed and used in cloud storage system. However, user
revocation is the primary issue in ABE schemes. In this article, we provide a ciphertext-policy
attribute based encryption (CP-ABE) scheme with efficient user revocation for cloud storage
system. The issue of user revocation can be solved efficiently by introducing the concept of user
group. When any user leaves, the group manager will update users‟ private keys except for those
who have been revoked. Additionally, CP-ABE scheme has heavy computation cost, as it grows
linearly with the complexity for the access structure. To reduce the computation cost, we
outsource high computation load to cloud service providers without leaking file content and
secret keys. Notbaly, our scheme can withstand collusion attack performed by revoked users
cooperating with existing users. We prove the security of our scheme under the divisible
computation Diffie-Hellman (DCDH) assumption. The result of our experiment shows
computation cost for local devices is relatively low and can be constant. Our scheme is suitable
for resource constrained devices.
IEEE Transactions on Services Computing (January 2016)

Real-time Tele-monitoring of Patients with Chronic Heart-Failure Using a Smartphone:
Lessons Learned
Abstract - We present a smartphone-based system for remote real-time tele-monitoring of
physical activity in patients with chronic heart-failure (CHF). We recently completed a pilot
study with 15 subjects to evaluate the feasibility of the proposed monitoring in the real world and
examine its requirements, privacy implications, usability, and other challenges encountered by
the participants and healthcare providers. Our tele-monitoring system was designed to assess
patient activity via minute-byminute energy expenditure (EE) estimated from accelerometry. In
addition, we tracked relative user location via global positioning system (GPS) to track outdoors
activity and measure walking distance. The system also administered daily-surveys to inquire
about vital signs and general cardiovascular symptoms. The collected data were securely
transmitted to a central server where they were analyzed in real time and were accessible to the
study medical staff to monitor patient health status and provide medical intervention if needed.
Although the system was designed for tele-monitoring individuals with CHF, the challenges,
privacy considerations, and lessons learned from this pilot study apply to other chronic health
conditions, such as diabetes and hypertension, that would benefit from continuous monitoring
through mobile-health (mHealth) technologies.
IEEE Transactions on Affective Computing (April 2016)
Intra-MARIO: A Fast Mobility Management Protocol for 6LoWPAN
Abstract - One of the major challenges in 6LoWPAN is to provide continuous services while
mobile nodes‟ movements with minimizing network inaccessible time caused due to handoffs.
Even though MIPv6, HMIPv6, and PMIPv6 are commonly accepted standards to address this in
IP networks, they cannot inherently avoid the degradation in communication quality during
handoff, since they are not designed with consideration of constrained node networks like
6LoWPAN. In this paper, we propose a new fast mobility management protocol for 6LoWPAN,

named intra-MARIO. To minimize handoff delay and enhance service availability, intra-MARIO
introduces three important components, which are a fast rejoin scheme for handoff management
with an adaptive polling-based movement detection and multi-hop pointer forwarding schemes
for location management. To justify the effectiveness, we have conducted the extensive
simulations by comparing intra-MARIO with prior schemes like a basic mobility management
scheme and a PMIPv6-based protocol. We then implement intra-MARIO on top of our
6LoWPAN platform (SNAIL) and evaluate the performance of intra-MARIO. The results
highlight that the intra-MARIO reduces overall handoff delay with low power consumption and
minimizes packet losses during handoffs, compared to prior mobility protocols.
IEEE Transactions on Mobile Computing (March 2016)
Hybrid Spectrum Sharing Through Adaptive Spectrum Handoff and Selection
Abstract - Spectrum sharing is a key function to provide fairness allocation as well as service
satisfaction across multiple users in cognitive radio networks. Even though spectrum sharing can
benefit from spectrum handoff to enhance rate performance by switching from unavailable
channels to available ones, the negative impact on handoff delay can cause significant service
degradation. In this work, we present a hybrid spectrum sharing strategy that includes novel
static and dynamic spectrum sharing algorithms based essentially on a rate compensation
approach and adapted best fit algorithms. The static scheme is applicable for some specific
network configurations where spectrum handoff is not necessary. Conversely, the dynamic
scheme can benefit from spectrum handoff to increase the achieved rate and also compensate for
the lost rate from the unavailable periods. These two sharing schemes are operated adaptively
according to the current network environment. We compare our hybrid strategy with a fully
dynamic one and an optimization framework. The proposed hybrid spectrum sharing
demonstrates its effectiveness in terms of improving the overall service satisfaction and reducing
the number of handoffs while the achieved rate is fulfilling compared to the optimal.
IEEE Transactions on Mobile Computing (January 2016)

Efficient Anonymous Message Submission
Abstract - In online surveys, many people are reluctant to provide true answers due to privacy
concerns. Thus, anonymity is important for online message collection. Existing solutions let each
member blindly shuffle the submitted messages by using an IND-CCA2 secure cryptosystem. In
the end, the message sender‟s identities are protected since no one knows the message
submission order. These approaches cannot efficiently handle groups of large size. In this paper,
we propose an efficient anonymous message submission protocol aimed at a practical group size.
Our protocol is based on a secret sharing scheme and a symmetric key cryptosystem. We propose
a novel method to aggregate members‟ messages into a message vector such that a group
member knows only his own position in the submission sequence. The protocol is accountable
for capturing malicious members breaking the protocol execution. We provide a theoretical proof
showing that our protocol is anonymous under malicious attacks. We also discuss our simulation
results to demonstrate the efficiency of our protocol.
IEEE Transactions on Dependable and Secure Computing (April 2016)
Proxies for Shortest Path and Distance Queries
Abstract - Computing shortest paths and distances is one of the fundamental problems on
graphs, and it remains a challenging task today. This article investigates a light-weight data
reduction technique for speeding-up shortest path and distance queries on large graphs. To do
this, we propose a notion of routing proxies (or simply proxies), each of which represents a small
subgraph, referred to as deterministic routing areas (dras). We first show that routing proxies
hold good properties for speeding-up shortest path and distance queries. Then, we design a
linear-time algorithm to compute routing proxies and their corresponding dras. Finally, we
experimentally verify that our solution is a general technique for reducing graph sizes and
speeding-up shortest path and distance queries, using real-life large graphs.
IEEE Transactions on Knowledge and Data Engineering (July 1 2016)

Quantifying Interdependent Privacy Risks with Location Data
Abstract - Co-location information about users is increasingly available online. For instance,
mobile users more and more frequently report their co-locations with other users in the messages
and in the pictures they post on social networking websites by tagging the names of the friends
they are with. The users‟ IP addresses also constitute a source of co-location information.
Combined with (possibly obfuscated) location information, such co-locations can be used to
improve the inference of the users‟ locations, thus further threatening their location privacy: As
co-location information is taken into account, not only a user‟s reported locations and mobility
patterns can be used to localize her, but also those of her friends (and the friends of their friends
and so on). In this paper, we study this problem by quantifying the effect of co-location
information on location privacy, considering an adversary such as a social network operator that
has access to such information. We formalize the problem and derive an optimal inference
algorithm that incorporates such co-location information, yet at the cost of high complexity. We
propose some approximate inference algorithms, including a solution that relies on the belief
propagation algorithm executed on a general Bayesian network model, and we extensively
evaluate their performance. Our experimental results show that, even in the case where the
adversary considers co-locations of the targeted user with a single friend, the median location
privacy of the user is decreased by up to 62% in a typical setting. We also study the effect of the
different parameters (e.g., the settings of the location-privacy protection mechanisms) in
different scenarios.
IEEE Transactions on Mobile Computing (May 2016)
On the Interplay Between Individuals’ Evolving Interaction Patterns and Traits in
Dynamic Multiplex Social Networks
Abstract - The interplay between individuals' social interactions and traits has been studied
extensively but traditionally from static or homogeneous social network perspectives. The recent
availability of dynamic and heterogeneous (multiplex) network data has introduced a variety of
new challenges. Critically, novel computational models are needed that can cope with data

dynamics and heterogeneity. In this paper, we introduce a computational framework that is
broadly applicable to a variety of dynamic, multiplex domains, which focuses on: 1) measuring
changes in node interaction patterns with time, 2) clustering nodes with similar evolving
patterns, and 3) linking the clusters with trait similarities. We apply the framework to study the
interplay between evolving topology and traits in an 18-month social network dataset
encompassing both digital communications and co-location instances. Notably, we demonstrate
how our framework captures results that would otherwise be missed by a simpler approach such
as static network analysis alone. In addition, we uncover network-trait interplays that have not
been studied to date and could lead to novel insights by domain scientists.
IEEE Transactions on Network Science and Engineering (March 2016)
Prius: Hybrid Edge Cloud and Client Adaptation for HTTP Adaptive Streaming in
Cellular Networks
Abstract - In this paper, we present Prius, a hybrid edge cloud and client adaptation framework
for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by
recent advances in edge cloud computing. In particular, emerging edge clouds are capable of
accessing application-layer and radio access networks (RAN) information in real time. Coupled
with powerful computation support, an edge cloud assisted strategy is expected to significantly
enrich mobile services. Meanwhile, although HAS has established itself as the dominant
technology for video streaming, one key challenge for adapting HAS to mobile cellular networks
is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the
highly dynamic cellular links. Edge cloud assisted HAS presents a new opportunity to resolve
these issues and achieve systematic enhancement of quality of experience (QoE) and QoE
fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of
adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering
the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN
channel status, client device characteristics as well as applicationlayer information in order to
jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the

cumulative viewing experience and an exponential smoothing estimation to accurately estimate
future channel under different moving patterns. Extensive trace-driven simulation results show
that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast
moving environment. Furthermore, Prius adaptation algorithm achieves a near-optimal
performance that outperforms exiting strategies.
IEEE Transactions on Circuits and Systems for Video Technology March 2016
Learning to Find Topic Experts in Twitter via Different Relations
Abstract - Expert finding has become a hot topic along with the flourishing of social networks,
such as micro-blogging services like Twitter. Finding experts in Twitter is an important problem
because tweets from experts are valuable sources that carry rich information (e.g., trends) in
various domains. However, previous methods cannot be directly applied to Twitter expert
finding problem. Recently, several attempts use the relations among users and Twitter Lists for
expert finding. Nevertheless, these approaches only partially utilize such relations. To this end,
we develop a probabilistic method to jointly exploit three types of relations (i.e., follower
relation, user-list relation, and list-list relation) for finding experts. Specifically, we propose a
Semi-Supervised Graph-based Ranking approach ($sf{SSGR}$) to offline calculate the global
authority of users. In $sf{SSGR}$, we employ a normalized Laplacian regularization term to
jointly explore the three relations, which is subject to the supervised information derived from
Twitter crowds. We then online compute the local relevance between users and the given query.
By leveraging the global authority and local relevance of users, we rank all of users and find top-
N users with highest ranking scores. Experiments on real-world data demonstrate the
effectiveness of our proposed approach for topic-specific expert finding in Twitt- r.
IEEE Transactions on Knowledge and Data Engineering July 2016
A Weighted Crowdsourcing Approach for Network Quality Measurement in Cellular Data
Networks

Abstract - With ubiquitous smartphone usages, it is important for network providers to provide
high-quality service to every user in the network. To make more effective planning and
scheduling, network providers need an accurate estimate of network quality for base stations and
cells from the perspective of user experience. Traditional drive testing approach provides a
quality measurement for each area and the quality measurement is obtained from the equipment
in a moving vehicle. This approach suffers from the limitations of high costs, low coverage, and
out-of-date values. In this paper, we propose a novel crowdsourcing approach for the task of
network quality estimation, which incurs little costs and provides timely and accurate quality
estimation. The proposed approach collects quality measurements from individual end users
within a certain network or cell coverage area, and then aggregates these measurements to obtain
a global measurement of network quality. We propose an effective aggregation scheme which
infers the information weights of end users and incorporates such weights into the estimation of
network quality. Experiments are conducted on two datasets collected from citywide 3G
networks, which involve 616; 796 users and 22; 715 cells. We validate the effectiveness of the
proposed approach compared with baseline method. From the aggregated measurement results,
we observe some interesting patterns about network quality, which can be explained by network
usage and traffic behavior. We also show that proposed approach runs in linear time.
IEEE Transactions on Mobile Computing March 2016
Enumerating Maximal Bicliques from a Large Graph using MapReduce
Abstract - We consider the enumeration of maximal bipartite cliques (bicliques) from a large
graph, a task central to many data mining problems arising in social network analysis and
bioinformatics. We present novel parallel algorithms for the MapReduce framework, and an
experimental evaluation using Hadoop MapReduce. Our algorithm is based on clustering the
input graph into smaller subgraphs, followed by processing different subgraphs in parallel. Our
algorithm uses two ideas that enable it to scale to large graphs: (1) the redundancy in work
between different subgraph explorations is minimized through a careful pruning of the search
space, and (2) the load on different reducers is balanced through a task assignment that is based

on an appropriate total order among the vertices. We show theoretically that our algorithm is
work optimal i.e. it performs the same total work as its sequential counterpart. We present a
detailed evaluation which shows that the algorithm scales to large graphs with millions of edges
and tens of millions of maximal bicliques. To our knowledge, this is the first work on maximal
biclique enumeration for graphs of this scale.
IEEE Transactions on Services Computing February 2016
PIF: A Personalized Fine-Grained Spam Filtering Scheme With Privacy Preservation in
Mobile Social Networks
Abstract - Mobile social network (MSN) emerges as a promising social network paradigm that
enables mobile users' information sharing in the proximity and facilitates their cyber-physical-
social interactions. As the advertisements, rumors, and spams spread in MSNs, it is necessary to
filter spams before they arrive at the recipients to make the MSN energy efficient. To this end,
we propose a personalized fine-grained filtering scheme (PIF) with privacy preservation in
MSNs. Specifically, we first develop a social-assisted filter distribution scheme, where the filter
creators send filters to their social friends (i.e., filter holders). These filter holders store filters
and decide to block spams or relay the desired packets through coarse-grained and fine-grained
keyword filtering schemes. Meanwhile, the developed cryptographic filtering schemes protect
creator's private information (i.e., keyword) embedded in the filters from directly disclosing to
other users. In addition, we establish a Merkle Hash tree to store filters as leaf nodes where filter
creators can check if the distributed filters need to be updated by retrieving the value of root
node. It is demonstrated that the PIF can protect users' private keywords included in the filter
from disclosure to others and detect forged filters. We also conduct the trace-driven simulations
to show that the PIF can not only filter spams efficiently but also achieve high delivery ratio and
low latency with acceptable resource consumption.
IEEE Transactions on Computational Social Systems February 2016

End-to-End Multiservice Delivery in Selfish Wireless Networks Under Distributed Node-
Selfishness Management
Abstract - In this paper, we investigate the multiservice delivery between the source-destination
pairs in distributed selfish wireless networks (SeWN), where selfish relay nodes (RN) expose
their selfish behaviors, i.e., forwarding or dropping multiservices. Owing to the effect of the
RNs' node-selfishness on the multiservices, a distributed framework of the node-selfishness
management is constructed to manage the RN's node-selfishness information (NSI) in terms of
its available resources, the employed incentive mechanism and the quality-of-service (QoS)
requirements, and the other RNs' NSI in terms of their historical behaviors. In this framework,
the RNs' NSI includes the degree of node-selfishness (DeNS), the degree of intrinsic selfishness
(DeIS) and the degree of extrinsic selfishness (DeES). Under the distributed node-selfishness
management, a path selection criterion is designed to select the most reliable and shortest path in
terms of RNs' DeISs affected by their available resources, and the optimal incentives are
determined by the source to stimulate forwarding multiservices of the RNs in the selected path.
Our simulation results demonstrate that this framework effectively manages the RNs' NSI, and
the optimal strategies of both the path selection and the incentives are determined.
IEEE Transactions on Communications March 2016
RSkNN: kNN Search on Road Networks by Incorporating Social Influence
Abstract - Although NN search on a road network, i.e., finding nearest objects to a query user
on, has been extensively studied, existing works neglected the fact that the 's social information
can play an important role in this NN query. Many real-world applications, such as location-
based social networking services, require such a query. In this paper, we study a new problem:
NN search on road networks by incorporating social influence (RSkNN). Specifically, the state-
of-the-art Independent Cascade (IC) model in social network is applied to de- ine social
influence. One critical challenge of the problem is to speed up the computation of the social
influence over large road and social networks. To address this challenge, we propose three
efficient index-based search algorithms, i.e., road network-based (RN-based), social network-

based (SN-based), and hybrid indexing algorithms. In the RN-based algorithm, we employ a
filtering-and-verification framework for tackling the hard problem of computing social influence.
In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query.
In the hybrid algorithm, we propose an index, summarizing the road and social networks, based
on which we can obtain query answers efficiently. Finally, we use real road and social network
data to empirically verify the efficiency and efficacy of our solutions.
IEEE Transactions on Knowledge and Data Engineering June 1 2016
Knowledge-Based Resource Allocation for Collaborative Simulation Development in a
Multi-tenant Cloud Computing Environment
Abstract - Cloud computing technologies have enabled a new paradigm for advanced product
development powered by the provision and subscription of computational services in a multi-
tenant distributed simulation environment. The description of computational resources and their
optimal allocation among tenants with different requirements holds the key to implementing
effective software systems for such a paradigm. To address this issue, a systematic framework
for monitoring, analyzing and improving system performance is proposed in this research.
Specifically, a radial basis function neural network is established to transform simulation tasks
with abstract descriptions into specific resource requirements in terms of their quantities and
qualities. Additionally, a novel mathematical model is constructed to represent the complex
resource allocation process in a multi-tenant computing environment by considering priority-
based tenant satisfaction, total computational cost and multi-level load balance. To achieve
optimal resource allocation, an improved multi-objective genetic algorithm is proposed based on
the elitist archive and the K-means approaches. As demonstrated in a case study, the proposed
framework and methods can effectively support the cloud simulation paradigm and efficiently
meet tenants‟ computational requirements in a distributed environment.
IEEE Transactions on Services Computing January 2016

Wireless Resource Scheduling Based on Backoff for Multi-user Multi-service Mobile Cloud
Computing
Abstract - Mobile cloud computing (MCC) can significantly improve the processing/storage
capacity and standby time of mobile terminals (MTs) by migrating data processing and storage to
remote cloud. However, due to the wireless resource limitations of access points/base stations,
data streaming of MCC suffers poor quality-of-service (QoS) in multi-user multi-service
scenarios, such as long buffering time and intermittent disruptions. In this paper, we propose a
Backoff based Wireless Resource Scheduling (BWRS) scheme, in which real-time services have
higher priority than non-real-time services. BWRS can improve the QoS of real-time streams and
overall performance of mobile cloud computing networks. We formulate an M/M/1 queueing
model and propose a Queueing-Delay-Optimal Control (QDOC) algorithm to minimize the
average queueing delay of nonreal- time services. Furthermore, a Delay-Constrained Control
(DCC) algorithm is developed not only to minimize the queueing delay of non-real-time services
of muti-service users, but also to support users‟ non-real-time services under delay constraints.
The simulation results show that the proposed scheme can minimize the average queueing delay
while still meeting delay requirement, and can significantly improve blocking probability and
channel utilization.
IEEE Transactions on Vehicular Technology February 2016
Assessing the Implications of Cellular Network Performance on Mobile Content Access
Abstract - Mobile applications such as VoIP, (live) gaming, or video streaming have diverse
QoS requirements ranging from low delay to high throughput. The optimization of the network
quality experienced by end-users requires detailed knowledge of the expected network
performance. Also, the achieved service quality is affected by a number of factors, including
network operator and available technologies. However, most studies measuring the cellular
network do not consider the performance implications of network configuration and
management. To this end, this paper reports about an extensive data set of cellular network
measurements, focused on analyzing root causes of mobile network performance variability.

Measurements conducted on a 4G cellular network in Germany show that management and
configuration decisions have a substantial impact on the performance. Specifically, it is observed
that the association of mobile devices to a Point of Presence (PoP) within the operator‟s network
can influence the end-to-end performance by a large extent. Given the collected data, a model
predicting the PoP assignment and its resulting RTT leveraging Markov Chain and machine
learning approaches is developed. RTT increases of 58% to 73% compared to the optimum
performance are observed in more than 57% of the measurements. Measurements of the response
and page load times of popular websites lead to similar results, namely a median increase of 40%
between the worst and the best performing PoP.
IEEE Transactions on Network and Service Management March 2016
Mobile Cloud Support for Semantic-enriched Speech Recognition in Social Care
Abstract - Nowadays, most users carry high computing power mobile devices where speech
recognition is certainly one of the main technologies available in every modern smartphone,
although battery draining and application performance (resource shortage) have a big impact on
the experienced quality. Shifting applications and services to the cloud may help to improve
mobile user satisfaction as demonstrated by several ongoing efforts in the mobile cloud area.
However, the quality of speech recognition is still not sufficient in many complex cases to
replace the common hand written text, especially when prompt reaction to short-term
provisioning requests is required. To address the new scenario, this paper proposes a mobile
cloud infrastructure to support the extraction of semantics information from speech recognition
in the Social Care domain, where carers have to speak about their patients conditions in order to
have reliable notes used afterward to plan the best support. We present not only an architecture
proposal, but also a real prototype that we have deployed and thoroughly assessed with different
queries, accents, and in presence of load peaks, in our experimental mobile cloud Platform as a
Service (PaaS) testbed based on Cloud Foundry.
IEEE Transactions on Cloud Computing May 2016

Robust Sparse Coding for Mobile Image Labeling on the Cloud
Abstract - With the rapid development of the mobile service and online social networking
service, a large of mobile images are generated and shared on the social networks every day. The
visual content of these images contains rich knowledge for many uses, such as social
categorization and recommendation. Mobile image labeling has therefore been proposed to
understand the visual content and received intensive attention in recent years. In this paper, we
present a novel mobile image labeling scheme on the cloud, in which mobile images are first and
efficiently transmitted to the cloud by Hamming compressed sensing (HCS) such that the heavy
computation for image understanding is transferred to the cloud for fast response to the queries
of users. On the cloud, we design a sparse correntropy framework for robustly learning the
semantic content of mobile images, based on which the relevant tags are assigned to the query
images. The proposed framework (called McMil) is very insensitive to noise and outliers, and is
optimized by a half-quadratic optimization technique. We theoretically show that our image
labeling approach is more robust than the squared loss, absolute loss, Cauchy loss and many
other robust loss function based sparse coding methods. To further understand the proposed
algorithm, we also derive its robustness and generalization error bounds. At last, we conduct
experiments on the PASCAL VOC‟07 dataset and empirically demonstrate the effectiveness of
the proposed robust sparse coding method for mobile image labeling.
IEEE Transactions on Circuits and Systems for Video Technology March 2016
Optimal Resource Sharing in 5G-enabled Vehicular Networks: A Matrix Game Approach
Abstract - Vehicular networks are expected to accommodate a large number of data-heavy
mobile devices and multi-application services. Whereas, it faces a significant challenge when we
need to deal with the ever-increasing demand of mobile traffic. In this paper, we present a new
paradigm of 5G-enabled vehicular networks to improve the network capacity and the system
computing capability. We extend the original cloud radio access network (C-RAN) to integrate
local cloud services to provide a low-cost, scalable, self-organizing and effective solution. The
new C-RAN is named as Enhanced C-RAN (EC-RAN). Cloudlets in EC-RAN are

geographically distributed for local services. Furthermore, Device-to-Device (D2D) and
Heterogeneous Networks (HetNet) are essential technologies in 5G systems. They can greatly
improve the spectrum efficiency and support the large-scale live video streaming in short-
distance communications. We exploit the matrix game theoretical approach to operate the
cloudlet resource management and allocation. Nash equilibrium solution can be obtained by
Karush-Kuhn-Tucker nonlinear complementarity approach. Illustrative results indicate that the
proposed resource sharing scheme with the geo-distributed cloudlets can improve the resource
utilization and reduce the system power consumption. Moreover, with the integration of the
Softwaredefine network (SDN) architecture, a vehicular network can easily reach a globally
optimal solution.
IEEE Transactions on Vehicular Technology March 2016
A Constraint Programming Scheduler for Heterogeneous High-Performance Computing
Machines
Abstract - Scheduling and dispatching tools for High-Performance Computing (HPC) machines
have the key role of mapping jobs to the available resources, trying to maximize performance
and Quality-of-Service (QoS). Allocation and Scheduling in the general case are well-known
NP-hard problems, forcing commercial schedulers to adopt greedy approaches to improve
performance and QoS. Searchbased approaches featuring the exploration of the solution space
have seldom been employed in this setting, but mostly applied in off-line scenarios. In this paper,
we present the first search-based approach to job allocation and scheduling for HPC machines,
working in a production environment. The scheduler is based on Constraint Programming, an
effective programming technique for optimization problems. The resulting scheduler is flexible,
as it can be easily customized for dealing with heterogeneous resources, user-defined constraints
and different metrics. We evaluate our solution both on virtual machines using synthetic
workloads, and on the Eurora HPC with production workloads. Tests on a wide range of
operating conditions show significant improvements in waitings and QoS in mid-tier HPC
machines w.r.t state-of-the-art commercial rule-based dispatchers. Furthermore, we analyze the

conditions under which our approach outperforms commercial approaches, to create a portfolio
of scheduling algorithms that ensures robustness, flexibility and scalability.
IEEE Transactions on Parallel and Distributed Systems January 2016
DistR: A Distributed Method for the Reachability Query over Large Uncertain Graphs
Abstract - Among uncertain graph queries, reachability, i.e., the probability that one vertex is
reachable from another, is likely the most fundamental one. Although this problem has been
studied within the field of network reliability, solutions are implemented on a single computer
and can only handle small graphs. However, as the size of graph applications continually
increases, the corresponding graph data can no longer fit within a single computer‟s memory and
must therefore be distributed across several machines. Furthermore, the computation of
probabilistic reachability queries is #P-complete making it very expensive even on small graphs.
In this paper, we develop an efficient distributed strategy, called DistR, to solve the problem of
reachability query over large uncertain graphs. Specifically, we perform the task in two steps:
distributed graph reduction and distributed consolidation. In the distributed graph reduction step,
we find all of the maximal subgraphs of the original graph, whose reachability probabilities can
be calculated in polynomial time, compute them and reduce the graph accordingly. After this
step, only a small graph remains. In the distributed consolidation step, we transform the problem
into a relational join process and provide an approximate answer to the #P-complete reachability
query. Extensive experimental studies show that our distributed approach is efficient in terms of
both computational and communication costs, and has high accuracy.
IEEE Transactions on Parallel and Distributed Systems February 2016
Quality-of-Experience-Oriented Autonomous Intersection Control in Vehicular Networks
Abstract - Recent advances in autonomous vehicles and vehicular communications are
envisioned to enable novel approaches to managing and controlling traffic intersections. In
particular, with intersection controller units (ICUs), passing vehicles can be instructed to cross

the intersection safely without traffic signals. Previous efforts on autonomous intersection
control mainly focused on guaranteeing the safe passage of vehicles and improving intersection
throughput, without considering the quality of the travel experience from the passengers'
perspective. In this paper, we aim to design an enhanced autonomous intersection control
mechanism, which not only ensures vehicle safety and enhances traffic efficiency but also cares
about the travel experience of passengers. In particular, we design the metric of smoothness to
quantitatively capture the quality of experience. In addition, we consider the travel time of
individual vehicles when passing the intersection in scheduling to avoid a long delay of some
vehicles, which not only helps with improving intersection throughput but also enhances the
system's fairness. With the above considerations, we formulate the intersection control model
and transform it into a convex optimization problem. On this basis, we propose a new algorithm
to achieve an optimal solution with low overhead. Finally, we build the simulation model and
implement the algorithm for performance evaluation. Comprehensive simulation results
demonstrate the superiority of the proposed algorithm.
IEEE Transactions on Intelligent Transportation Systems February 2016
Visual Analysis of Cloud Computing Performance Using Behavioral Lines
Abstract - Cloud computing is an essential technology to Big Data analytics and services. A
cloud computing system is often comprised of a large number of parallel computing and storage
devices. Monitoring the usage and performance of such a system is important for efficient
operations, maintenance, and security. Tracing every application on a large cloud system is
untenable due to scale and privacy issues. But profile data can be collected relatively efficiently
by regularly sampling the state of the system, including properties such as CPU load, memory
usage, network usage, and others, creating a set of multivariate time series for each system.
Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper,
we present a visual based analysis approach to understanding and analyzing the performance and
behavior of cloud computing systems. Our design is based on similarity measures and a layout
method to portray the behavior of each compute node over time. When visualizing a large

number of behavioral lines together, distinct patterns often appear suggesting particular types of
performance bottleneck. The resulting system provides multiple linked views, which allow the
user to interactively explore the data by examining the data or a selected subset at different levels
of detail. Our case studies, which use datasets collected from two different cloud systems, show
that this visual based approach is effective in identifying trends and anomalies of the systems.
IEEE Transactions on Visualization and Computer Graphics June 2016
Resource Dependency Processing in Web Scaling Frameworks
Abstract - The upsurge of mobile devices paired with highly interactive social web applications
generates enormous amounts of requests web services have to deal with. Consequently in our
previous work, a novel request flow scheme with scalable components was proposed for storing
interdependent, permanently updated resources in a database. The major challenge is to process
dependencies in an optimal fashion while maintaining dependency constraints. In this work,
three research objectives are evaluated by examining resource dependencies and their key graph
measurements. An all-sources longest-path algorithm is presented for efficient processing and
dependencies are analysed to find correlations between performance and graph measures. Two
algorithms basing their parameters on six real-world web service structures, e.g. Facebook Graph
API are developed to generate dependency graphs and a model is developed to estimate
performance based on resource parameters. An evaluation of four graph series discusses
performance effects of different graph structures. The results of an evaluation of 2000 web
services with over 850 thousand resources and 6 million requests indicate that resource
dependency processing can be up to a factor of two faster compared to a traditional processing
approach while an average model fit of 97% allows an accurate prediction.
IEEE Transactions on Services Computing May 2016

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