The document presents a study on QoS-aware data replication for data-intensive applications in cloud computing, proposing two algorithms to improve data replication costs and minimize QoS violations. The first algorithm is a greedy method, while the second transforms the problem into a minimum-cost maximum-flow format for optimal solutions in polynomial time, although with greater computational demand. The proposal includes techniques to reduce data replication time while ensuring data availability and aligning with application QoS requirements.

1. QOS-AWARE DATA REPLICATION FOR
DATA-INTENSIVE APPLICATIONS IN
CLOUD COMPUTING SYSTEMS
Presented by:
LansA Informatics Pvt Ltd

2. ABSTRACT
• Cloud computing provides scalable computing and storage resources. More and more data-intensive
applications are developed in this computing environment. Different applications
have different quality-of-service (QoS) requirements. To continuously support the QoS
requirement of an application after data corruption, we propose two QoS-aware data
replication (QADR) algorithms in cloud computing systems.
• The first algorithm adopts the intuitive idea of high-QoS first-replication (HQFR) to perform
data replication. However, this greedy algorithm cannot minimize the data replication cost
and the number of QoS-violated data replicas. To achieve these two minimum objectives, the
second algorithm transforms the QADR problem into the well-known minimum-cost
maximum-flow (MCMF) problem.
• By applying the existing MCMF algorithm to solve the QADR problem, the second algorithm
can produce the optimal solution to the QADR problem in polynomial time, but it takes more
computational time than the first algorithm. Moreover, it is known that a cloud computing
system usually has a large number of nodes. We also propose node combination techniques
to reduce the possibly large data replication time. Finally, simulation experiments are
performed to demonstrate the effectiveness of the proposed algorithms in the data
replication and recovery.

3. Existing System
• Due to a large number of nodes in the cloud computing
system, the probability of hardware failures is nontrivial based
on the statistical analysis of hardware failures. Some hardware
failures will damage the disk data of nodes. As a result, the
running data-intensive applications may not read data from
disks successfully.
• To tolerate the data corruption, the data replication technique
is extensively adopted in the cloud computing system to
provide high data availability. For example, the Amazon EC2 is
a realistic heterogeneous cloud platform, which provides
various infrastructure resource types to meet different user
needs in the computing and storage resources.
• The cloud computing system has heterogeneous
characteristics in nodes. Note that the QoS requirement of an
application is defined from the aspect of the request
information. For example, in, the response time of a data
object access is defined as the QoS requirement of an
application in the content distribution system.

4. DISADVANTAGES
• The QoS requirement of an application is not taken
into account in the data replication. When data
corruption occurs, the QoS requirement of the
application cannot be supported continuously.
• The data of a high-QoS application may be
replicated in a low-performance node (the node
with slow communication and disk access latencies).
Later, if data corruption occurs in the node running
the high-QoS application, the data of the application
will be retrieved from the low-performance node.
• Since the low-performance node has slow
communication and disk access latencies, the QoS
requirement of the high-QoS application may be
violated.

5. PROPOSED SYSTEM
• We Propose QoS-aware data replication (QADR) problem for data-intensive applications
in cloud computing systems. The QADR problem concerns how to efficiently consider the
QoS requirements of applications in the data replication. This can significantly reduce the
probability that the data corruption occurs before completing data replication. Due to
limited replication space of a storage node, the data replicas of some applications may be
stored in lower-performance nodes. This will result in some data replicas that cannot
meet the QoS requirements of their corresponding applications. These data replicas are
called the QoS-violated data replicas. The number of QoS-violated data replicas is
expected to be as small as possible.
• To solve the QADR problem, we first propose a greedy algorithm, called the high-QoS
first-replication (HQFR) algorithm. In this algorithm, if application i has a higher QoS
requirement, it will take precedence over other applications to perform data replication.
However, the HQFR algorithm cannot achieve the above minimum objective. Basically,
the optimal solution of the QADR problem can be obtained by formulating the problem as
an integer linear programming (ILP) formulation. However, the ILP formulation
involves complicated computation. To find the optimal solution of the QADR problem in
an efficient manner, we propose a new algorithm to solve the QADR problem. In this
algorithm, the QADR problem is transformed to the minimum-cost maximum-flow
(MCMF) problem.
• We propose a new algorithm to solve the QADR problem. In this algorithm, the
QADR problem is transformed to the minimum-cost maximum-flow (MCMF) problem.
Then, an existing MCMF algorithm is utilized to optimally solve the QADR problem in
polynomial time. Compared to the HQFR algorithm, the optimal algorithm takes more
computational time.

6. ADVANTAGES
• While minimizing the data replication cost,
the data replication can be completed
quickly.
• We use node combination techniques to
suppress the computational time of the
QADR problem without linear growth as
increasing the number of nodes

7. SYSTEM ARCHITECTURE

8. SYSTEM CONFIGURATION
HARDWARE REQUIREMENTS:-
• Processor - Pentium –IV
• Speed - 1.1 Ghz
• RAM - 512 MB(min)
• Hard Disk - 40 GB
• Key Board - Standard Windows Keyboard
• Mouse - Two or Three Button Mouse
• Monitor - LCD/LED
SOFTWARE REQUIREMENTS:
• Operating system : Windows XP.
• Coding Language : C# .Net
• Data Base : SQL Server 2005
• Tool : VISUAL STUDIO 2008.

9. REFERENCE
• Jenn-Wei Lin, Chien-Hung Chen, and J. Morris
Chang, “QOS-AWARE DATA REPLICATION FOR DATA-INTENSIVE
APPLICATIONS IN CLOUD COMPUTING
SYSTEMS” IEEE TRANSACTIONS ON CLOUD
COMPUTING, VOL. 1, NO. 1, JANUARY-JUNE 2013

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Coimbatore - 641 015
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