Secure and Efficient Client and Server Side Data Deduplication to Reduce Storage in Remote Cloud Computing Systems

IDL - International Digital Library Of
Technology & Research
Volume 1, Issue 5, May 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 1|P a g e Copyright@IDL-2017
Secure and Efficient Client and Server Side
Data Deduplication to Reduce Storage in
Remote Cloud Computing Systems
Hemanth Chandra N1
, Sahana D. Gowda2
,
Dept. of Computer Science
1 B.N.M Institute of Technology, Bangalore, India
2 B.N.M Institute of Technology, Bangalore, India
Abstract: Duplication of data in storage systems is
becoming increasingly common problem. The system
introduces I/O Deduplication, a storage optimization
that utilizes content similarity for improving I/O
performance by eliminating I/O operations and
reducing the mechanical delays during I/O operations
and shares data with existing users if Deduplication
found on the client or server side. I/O Deduplication
consists of three main techniques: content-based
caching, dynamic replica retrieval and selective
duplication. Each of these techniques is motivated by
our observations with I/O workload traces obtained
from actively-used production storage systems, all of
which revealed surprisingly high levels of content
similarity for both stored and accessed data.
Keywords: Deduplication, POD, Data Redundancy,
Storage Optimization, iDedup, I/O Deduplication,
I/O Performance.
1. INTRODUCTION
Duplication of data in primary storage systems is quite
common due to the technological trends that have been
driving storage capacity consolidation the elimination
of duplicate content at both the file and block levels
for improving storage space utilization is an active
area of research. Indeed, eliminating most duplicate
content is inevitable in capacity sensitive applications
such as archival storage for cost effectiveness. On the
other hand, there exist systems with a moderate degree
of content similarity in their primary storage such as
email servers, virtualized servers and NAS devices
running file and version control servers. In case of
email servers, mailing lists, circulated attachments and
SPAM can lead to duplication. Virtual machines may
run similar software and thus create collocated
duplicate content across their virtual disks. Finally, file
and version control systems servers of collaborative
groups often store copies of the same documents,
sources and executables. In such systems, if the degree
of content similarity is not overwhelming, eliminating
duplicate data may not be a primary concern.
Gray and Shenoy[1] have pointed out that given the
technology trends for price-capacity and price-
performance of memory/disk sizes and disk accesses
respectively, disk data must “cool” at the rate of 10X
per decade. They suggest data replication as a means
to this end. An instantiation of this suggestion is
intrinsic replication of data created due to
consolidation as seen now in many storage systems,
including the ones illustrated earlier. Here, it is
referring to intrinsic (or application/user generated)
data replication as opposed to forced (system
generated) redundancy such as in a RAID-1 storage
system. In such systems, capacity constraints are
invariably secondary to I/O performance.

On-disk duplication of content and I/O traces obtained
from three varied production systems at Florida
International University(FIU) that included a
virtualized host running two department web-servers,
the department email server, and a file server for our
research group has been analyzed. Three observations
have been made from the analysis of these traces.
First, our analysis revealed significant levels of both
disk static similarity and workload static similarity
within each of these systems. Disk static similarity is
an indicator of the amount of duplicate content in the
storage medium, while workload static similarity
indicates the degree of on-disk duplicate content
accessed by the I/O workload. These similarity
measures have been defined formally in § 2. Second, a
consistent and marked discrepancy was discovered
between reuse distances for sector and content in the
I/O accesses on these systems indicating that content
is reused more frequently than sectors. Third, there is
significant overlap in content accessed over successive
intervals of longer time-frames such as days or weeks.
Based on the observations, the premise that intrinsic
content similarity is explored in storage systems and
access to replicated content within I/O workloads can
both be utilized to improve I/O performance. In doing
so, a storage optimization that utilizes content
similarity to either eliminate I/O operations altogether
or optimize the resulting disk head movement within
the storage system is designed and evaluate in I/O
Deduplication. I/O Deduplication comprises three key
techniques: (i)Content-based caching that uses the
popularity of “data content” rather than “data location”
of I/O accesses in making caching decisions,
(ii)Dynamic replica retrieval that upon a cache miss
for a read operation, dynamically chooses to retrieve a
content replica which minimizes disk head movement,
and (iii)Selective duplication that dynamically
replicates frequently accessed content in scratch space
that is distributed over the entire storage medium to
increase the effectiveness of dynamic replica retrieval.
2. RELATED WORK
This paper is related to works on Deduplication
concepts in primary memory. Nimrod Megiddo and
Dharmendra S. Modha[20] discussed about the
problem of cache management in a demand paging
scenario with uniform page sizes. A new cache
management policy is proposed, namely, Adaptive
Replacement Cache (ARC), that has several
advantages. In response to evolving and changing
access patterns, ARC dynamically, adaptively and
continually balances between the recency and
frequency components in an online and self tuning
fashion. The policy ARC uses a learning rule to
adaptively and continually revise its assumptions
about the workload. The policy ARC is empirically
universal, that is, it empirically performs as well as a
certain fixed replacement policy– even when the latter
uses the best workload-specific tuning parameter that
was selected in an offline fashion. Consequently, ARC
works uniformly well across varied workloads and
cache sizes without any need for workload specific a
priori knowledge or tuning. Various policies such as
LRU-2, 2Q, LRFU and LIRS require user-defined
parameters, and unfortunately, no single choice works
uniformly well across different workloads and cache
sizes. The policy ARC is simple-to-implement and
like LRU, has constant complexity per request. In
comparison, policies LRU-2 and LRFU both require
logarithmic time complexity in the cache size. The
policy ARC is scan-resistant: it allows one-time
sequential requests to pass through without polluting
the cache. On real-life traces drawn from numerous
domains, ARC leads to substantial performance gains
over LRU for a wide range of cache sizes.
Aayush Gupta et.al[22] have designed CA-SSD which
employs content-addressable storage (CAS) to exploit
such locality. Our CA-SSD design employs
enhancements primarily in the flash translation layer
(FTL) with minimal additional hardware, suggesting
its feasibility. Using three real-world workloads with
content information, statistical characterizations of two
aspects of value locality - value popularity and
temporal value locality - that form the foundation of

CA-SSD is devised. CA-SSD is able to reduce average
response times by about 59-84% compared to
traditional SSDs. Even for workloads with little or no
value locality, CA-SSD continues to offer comparable
performance to a traditional SSD. Our findings
advocate adoption of CAS in SSDs, paving the way
for a new generation of these devices.
Chi Zhang, Xiang Yu, Y. Wang[23] have answered
two key questions. First, since both eager-writing and
mirroring rely on extra capacity to deliver
performance improvements, how to satisfy competing
resource demands given a fixed amount of total disk
space? Second, since eager-writing allows data to be
dynamically located, how to exploit this high degree
of location independence in an intelligent disk
scheduler? In this paper, the two key questions were
addressed and compared the resulting EW-Array
prototype performance against that of conventional
approaches. The experimental results demonstrate that
the eager writing disk array is an effective approach to
providing scalable performance for an important class
of transaction processing applications.
Kiran Srinivasan et.al[24] has proposed an inline
Deduplication solution, iDedup, for primary
workloads, while minimizing extra IOs and seeks. Our
algorithm is based on two key insights from real world
workloads: i) spatial locality exists in duplicated
primary data and ii) temporal locality exists in the
access patterns of duplicated data. Using the first
insight, only sequences of disk blocks were selectively
deduplicated. This reduces fragmentation and
amortizes the seeks caused by Deduplication. The
second insight allows us to replace the expensive, on-
disk, Deduplication metadata with a smaller, in-
memory cache. These techniques enable us to tradeoff
capacity savings for performance, as demonstrated in
our evaluation with real-world workloads. Our
evaluation shows that iDedup achieves 60-70% of the
maximum Deduplication with less than a 5% CPU
overhead and a 2-4% latency impact.
Jiri Schindler et.al[26] introduce proximal I/O, a new
technique for improving random disk I/O performance
in file systems. The key enabling technology for
proximal I/O is the ability of disk drives to retire
multiple I/O’s, spread across dozens of tracks, in a
single revolution. Compared to traditional update-in-
place or write-anywhere file systems, this technique
can provide a nearly seven-fold improvement in
random I/O performance while maintaining (near)
sequential on-disk layout. This paper quantifies
proximal I/O performance and proposes a simple data
layout engine that uses a flash memory-based write
cache to aggregate random updates until they have
sufficient density to exploit proximal I/O. The results
show that with cache of just 1% of the overall disk-
based storage capacity, it is possible to service 5.3 user
I/O requests per revolution for random updates
workload. On an aged file system, the layout can
sustain serial read bandwidth within 3% of the best
case. Despite using flash memory, the overall system
cost is just one third of that of a system with the
requisite number of spindles to achieve the equivalent
number of random I/O operations.
2.1 EXISTING SYSTEM
To address the existing data Deduplication schemes for
primary storage, such as iDedup and Offline-Dedupe,
are capacity oriented in that they focus on storage
capacity savings and only select the large requests to
deduplicate and bypass all the small requests (e.g., 4
KB, 8 KB or less). The rationale is that the small I/O
requests only account for a tiny fraction of the storage
capacity requirement, making Deduplication on them
unprofitable and potentially counterproductive
considering the substantial Deduplication overhead
involved. However, previous workload studies have
revealed that small files dominate in primary storage
systems (more than 50 percent) and are at the root of the
system performance bottleneck. Furthermore, due to the
buffer effect, primary storage workloads exhibit obvious
I/O burstiness. The important performance issue of
primary storage in the Cloud and the above
Deduplication-induced problems, a Performance-
Oriented data Deduplication scheme, called POD, rather
than a capacity-oriented one (e.g., iDedup), to improve
the I/O performance of primary storage systems in the

Cloud is proposed. Figure 1 represents the architecture
of POD. By considering the workload characteristics.
POD takes a two-pronged approach to improving the
performance of primary storage systems and minimizing
performance overhead of Deduplication, namely, a
request-based selective Deduplication technique, called
Select-Dedup, to alleviate the data fragmentation and an
adaptive memory management scheme, called iCache,
to ease the memory contention between the bursty read
traffic and the bursty write traffic.
2.2 PROPOSED SYSTEM(POD vs IDedup)
A possible future direction is to optionally coalesce or
even eliminate altogether write I/O operations for
content that are already duplicated elsewhere on the
disk, or alternatively direct such writes to alternate
locations in the scratch space.
Figure 1. Architecture diagram of POD.
While the first option might seem similar to data
Deduplication at a high-level, a primary focus on the
performance implications of such optimizations rather
than capacity improvements has been suggested. Any
optimization for writes affects the read-side
optimizations of I/O Deduplication and a careful
analysis and evaluation of the trade-off points in this
design space is important and shares data with existing
users if Deduplication found in the client or server side.
Advantages:
1.Requires less space in the storage server
2.Data Deduplication will be done completely in all
blocks
3. IMPLEMENTATION
POD
In this paper, the system proposes POD, a
performance-oriented Deduplication scheme, to
improve the performance of primary storage systems
in the Cloud by leveraging data Deduplication on the
I/O path to remove redundant write requests while also
saving storage space. In Figure 2 shows the sequence
diagram where one can understand how the sequence
flow happens between each module. POD takes a
request-based selective Deduplication approach
(Select-Dedupe) to Deduplicating the I/O redundancy
on the critical I/O path in such a way that it minimizes
the data fragmentation problem. In the meanwhile, an
intelligent cache management (iCache) is employed in
POD to further improve read performance and
increase space saving, by adapting to I/O burstiness.
Our extensive trace-driven evaluations show that POD
significantly improves the performance and saves the
capacity of primary storage systems in the Cloud.

Figure 2. Sequence diagram of POD.
iDedup
In iDedup system, the system describes iDedup, an
inline Deduplication system specifically targeting
latency-sensitive, primary storage workloads and
comparison with latest techniques like POD also
known as I/O Deduplication. With latency sensitive
workloads, inline Deduplication has many
challenges: fragmentation leading to extra disk seeks
for reads, Deduplication processing overheads in the
critical path and extra latency caused by IOs for
Dedup-metadata management.
3.1 COMPARATIVE ANALYSIS
In this section, the performance of POD vs iDedup
Deduplication models is evaluated through extensive
trace-driven experiments.
(a) Time delay in iDedup
(b) Time delay in POD
Figure 3.The time delay performance of the different
Deduplication schemes.
A prototype of POD as a module in the Linux
operating system and use the trace-driven
experiments to evaluate its effectiveness and
efficiency has been implemented. In this paper, POD
with the capacity oriented scheme iDedup is
compared. Two models (POD vs iDedup) with two
parameters are compared, they are Time delay and
Cpu utilization.

(a) Cpu utilization in iDedup
(b) Cpu utilization in POD
Figure 4. The Cpu utilization performance of the
different Deduplication schemes.
In the first parameter comparison i.e., Time delay.
Four production files were compared for our trace
driven evaluation. In Figure 3(a) the time delay for
the iDedup model can be seen and in Figure 3(b) the
time delay for same files in Pod model is shown. For
both the deduplicator models, same files were
uploaded with name cmain, owner, clog, search.
Results shows that Pod is efficient than iDedup.
In the second parameter comparison i.e., Cpu
utilization. a single file named abc.txt was uploaded
to both Pod and iDedup models. In Figure 4(a) Cpu
utilization in iDedup model can be seen and in
Figure 4(b) the Cpu utilization in Pod model can be
seen. From the results it states that Pod is more
efficient than the iDedup model.
4. CONCLUSION
System and storage consolidation trends are driving
increased duplication of data within storage systems.
Past efforts have been primarily directed towards the
elimination of such duplication for improving storage
capacity utilization. With I/O Deduplication, a
contrary view is taken that intrinsic duplication in a
class of systems which are not capacity-bound can be
effectively utilized to improve I/O performance – the
traditional Achilles’ heel for storage systems. Three
techniques contained within I/O Deduplication work
together to either optimize I/O operations or eliminate
them altogether. An in-depth evaluation of these
mechanisms revealed that together they reduced
average disk I/O times by 28-47%, a large
improvement all of which can directly impact the
overall application-level performance of disk I/O
bound systems. The content-based caching mechanism
increased memory caching effectiveness by increasing
cache hit rates by 10% to 4x for read operations when
compared to traditional sector-based caching. Head-
position aware dynamic replica retrieval directed I/O
operations to alternate locations on-the-fly and
additionally reduced I/O times by 10-20%. Selective
duplication created additional replicas of popular
content during periods of low foreground I/O activity
and further improved the effectiveness of dynamic
replica retrieval by 23 35%.
FUTURE WORK
I/O Deduplication opens up several directions for
future work. One avenue for future work is to explore
content-based optimizations for write I/O operations.
A possible future direction is to optionally coalesce or
even eliminate altogether write I/O operations for
content that are already duplicated elsewhere on the
disk or alternatively direct such writes to alternate
locations in the scratch space. While the first option
might seem similar to data Deduplication at a high-
level, a primary focus on the performance implications

of such optimizations rather than capacity
improvements is suggested. Any optimization for
writes affects the read-side optimizations of I/O
Deduplication and a careful analysis and evaluation of
the trade-off points in this design space is important
and shares data with existing users if Deduplication
found in the client or server side.
REFERENCES
[1] Jim Gray and Prashant Shenoy. Rules of Thumb in
Data Engineering. Proc. of the IEEE International
Conference on Data Engineering, February 2000.
[2] Charles B. Morrey III and Dirk Grunwald.
Peabody: The Time Travelling Disk. In Proc. of the
IEEE/NASA MSST, 2003.
[3] Windsor W. Hsu, Alan Jay Smith, and Honesty C.
Young. The Automatic Improvement of Locality in
Storage Systems. ACM Transactions on Computer
Systems, 23(4):424–473, Nov 2005.
[4] S. Quinlan and S. Dorward. Venti: A New
Approach to Archival Storage. Proc. of the USENIX
Annual Technical Conference on File and Storage
Technologies, January 2002.
[5] Cyril U. Orji and Jon A. Solworth. Doubly
distorted mirrors. In Proceedings of the ACM
SIGMOD, 1993.
[6] Medha Bhadkamkar, Jorge Guerra, Luis Useche,
Sam Burnett, Jason Liptak, Raju Rangaswami, and
Vagelis Hristidis. BORG: Block-reORGanization for
Selfoptimizing Storage Systems. In Proc. of the
USENIX Annual Technical Conference on File and
Storage Technologies, February 2009.
[7] Sergey Brin, James Davis, and Hector Garcia-
Molina. Copy Detection Mechanisms for Digital
Documents. In Proc. of ACM SIGMOD, May 1995.
[8] Austin Clements, Irfan Ahmad, Murali Vilayannur,
and Jinyuan Li. Decentralized Deduplication in san
cluster file systems. In Proc. of the USENIX Annual
Technical Conference, June 2009.
[9] Burton H. Bloom. Space/time trade-offs in hash
coding with allowable errors. Communications of the
ACM, 13(7):422–426, 1970.
[10] Daniel Ellard, Jonathan Ledlie, Pia Malkani, and
Margo Seltzer. Passive NFS Tracing of Email and
Research Workloads. In Proc. of the USENIX Annual
Technical Conferenceon File and Storage
Technologies, March 2003.
[11] P. Kulkarni, F. Douglis, J. D. LaVoie, and J. M.
Tracey. Redundancy Elimination Within Large
Collections of Files. Proc. of the USENIX Annual
Technical Conference, 2004.
[12] Binny S. Gill. On multi-level exclusive caching:
offline optimality and why promotions are better than
demotions. In Proc. of the USENIX Annual Technical
Conference on File and Storage Technologies,
Feburary 2008.
[13] Diwaker Gupta, Sangmin Lee, Michael Vrable,
Stefan Savage, Alex C. Snoeren, George Varghese,
Geoffrey Voelker, and Amin Vahdat. Difference
Engine: Harnessing Memory Redundancy in Virtual
Machines. Proc. Of the USENIX OSDI, December
2008.
[14] Hai Huang, Wanda Hung, and Kang G. Shin.
FS2: Dynamic Data Replication In Free Disk Space
For Improving Disk Performance And Energy
Consumption. In Proc. of the ACM SOSP, October
2005.
[15] Jorge Guerra, Luis Useche, Medha Bhadkamkar,
Ricardo Koller, and Raju Rangaswami. The Case for
Active Block Layer Extensions. ACM Operating
Systems Review,42(6), October 2008.
[16] N. Jain, M. Dahlin, and R. Tewari. TAPER:
Tiered Approach for Eliminating Redundancy in

Replica Synchronization. In Proc. of the USENIX
Conference on File and Storage Systems, 2005.
[17] Song Jiang, Feng Chen, and Xiaodong Zhang.
Clock-pro: An effective improvement of the clock
replacement. In Proc. of the USENIX Annual
Technical Conference, April 2005.
[18] Andrew Leung, Shankar Pasupathy, Garth
Goodson and Ethan Miller. Measurement and Analysis
of Large-Scale Network File System Workloads. Proc.
of the USENIX Annual Technical Conference, June
2008.
[19] Xuhui Li, Ashraf Aboulnaga, Kenneth Salem,
Aamer Sachedina, and Shaobo Gao. Second-tier cache
management using write hints. In Proc. of the
Storage Technologies, 2005.
[20] Nimrod Megiddo and D. S. Modha. Arc: A self-
tuning, low overhead replacement cache. In Proc. of
Storage Technologies, 2003.
[21] R. L. Mattson, J. Gecsei, D. R. Slutz, and I. L.
Traiger. Evaluation techniques for storage hierarchies.
IBM Systems Journal, 9(2):78–117, 1970.
[22] Aayush Gupta, Raghav Pisolkar, Bhuvan
Urgaonkar, and Anand Sivasubramaniam. Leveraging
Value Locality in Optimizing NAND Flash-based
SSDs, 2011.
[23] Chi Zhang, Xiang Yu, Y. Wang. Configuring and
Scheduling an Eager-Writing Disk Array for a
Transaction Processing Workload. 2002.
[24] Kiran Srinivasan, Tim Bisson, Garth Goodson,
Kaladhar Voruganti. iDedup: Latency-aware, Inline
Data Deduplication for Primary Storage. 2012.
[25] Dina Bitton and Jim Gray. Disk Shadowing. In
Proc. Of the International Conference on Very Large
Data Bases, 1988.
[26] Jiri Schindler, Sandip Shete, Keith A. Smith.
Improving Throughput for Small Disk Requests with
Proximal I/O. 2011.
[27] Mark Lillibridge, Kave Eshghi, Deepavali
Bhagwat, Vinay Deolalikar, Greg Trezise, and Peter
Camble. Sparse indexing: large scale, inline
deduplication using sampling and locality. In Proc. of
the USENIX Annual Technical Conference on File
and Storage Technologies, February 2009.

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