IRJET-Consistency As A Service In Public Cloud

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395 -0056

Volume: 02 Issue: 03 | June-2015

p-ISSN: 2395-0072

www.irjet.net

Consistency As A Service In Public Cloud
Dhnashree.B.Sawale 1, Resmi.S2
M.tech , Dept. of CSE, Atria Institute of Technology , Bengaluru, Karnataka, India
Assistant Professor Dept. of CSE, Atria Institute of Technology , Bengaluru, Karnataka, India
1

2

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Abstract - A Cloud storage service has become
popular due to a very great advantages .To provide
everywhere always –on access , a cloud service provider
support many replicas for every one piece of data on
distributed server . A problem of using the replication
technique in cloud is very costly to accomplish strong
consistency on a worldwide scale . A novel consistency as a
service model , contain large data cloud and many small
audit clouds . In the consistency as a service model , data
cloud is supported through a cloud service provider and
collection of users that constitute an audit cloud be able to
validate whether the data cloud provides the promised level
of consistency or not .It proposed a two-level auditing
architecture ,which simply needs a loosely synchronized
cloud in the audit cloud .Then design algorithm to quantify
the severity of violations with two metrices : the
commonality of violations and staleness of the value of read
. Heuristic auditing strategy (HAS) is used to expose as many
violations as possible .

Key

Words:

Cloud

storage

,

consistency

as

non relational data store . Support store and query
function usually provided only by relational database and
it also hold to increase performance web application . User
can store and query data item by means of web service
request .it manages manually the infrastructure
provisioning and hardware, software maintenance,
replication and indexing of data items. By use the Cloud
storage services, the clients can access data stored in a
cloud anytime and anywhere using any device, without
any capital investment when they are deploying the
underlying hardware infrastructures.

a

service(Caas), two-level auditing ,heuristic auditing
strategy(HAS)

1. INTRODUCTION
Now a day’s Cloud computing has become more
popular, as it provide advantages like security, scalability,
elasticity and high availability at lower cost [6][8] .Cloud
storage service have become more accepted due to their
very great advantages .Cloud service provider retain many
replicas for every piece of data on physically distributed
server. Replication method is used to improve
performance and increase reliability .Replica it allows
remote sites to go on working in the event of local failure.
To maintain continuous accessibility the file is replicated
at many different places in cloud so even if one of the site
is down still you can retrieve the data from another place
.Cloud storage services which involves the transfer of data
storage as a service including data base like services and
NAS( network attach storage )frequently billed on service
computing basis. Example Amazon simple database it is
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Fig 1 System Architecture
Fig Show system architecture . In this there are two user
Kumar and Geetha they both are working on Project using
a Cloud Storage Service. Data cloud that have data that
data is replicated at many places. Data is replicated to five
cloud server that is CS1,CS2,CS3,CS4,CS5 .After uploading a
new document to CS4 ,Geetha calls Kumar to download
the latest version for integrated design .Here ,after Geetha
calls Kumar, the causal relationship is accepted between
Geetha’s update and Kumar’s read .
Therefore , the cloud should give causal
consistency ,which ensures that Geetha’s update is
committed to all of the replicas before Kumar’s read . If
the cloud provides only eventual consistency , then Kumar
is allowed to access an old version of the requirement
analysis from CS5 .In this case ,the integrated design that

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395 -0056

Volume: 02 Issue: 03 | June-2015

p-ISSN: 2395-0072

www.irjet.net

is based on an old version may not satisfy the real
requirements of customers. Different applications have
different consistency requirements. The consistency
properly ensure that any transaction will bring the
database from one valid state to another. Any data written
to database must be valid according to all defined rules,
including constraints , cascades, triggers and any
combination thereof . Different application have different
consistency requirement .For example mail services
require both monotonic-read consistency and read-yourwrite consistency and social network services need casual
consistency [7] .In cloud storage, consistency not only
determines accuracy but also the real cost per transaction.
A novel consistency as a service (CaaS) model consists of
big data cloud and several small audit clouds . The
implementation of the data cloud is not clear to all users
due to the virtualization technique . So it is very difficult
for users to verify whether each replica in the data cloud is
the latest one or not . Local auditing focuses on
monotonic–read and read-your-write consistencies which
can be performed by a light-weight online algorithm .
Global auditing focuses on casual consistency which is
performed by constructing a directed graph .

2. RELATED WORK
It present a novel approach to benchmark
staleness in distributed datastores and make use of the
approach to assess Amazon’s Simple Storage Service (S3)
[4] There are two main classes of consistency :datacentric and client-centric consistency. Data-centric
consistency model generally focus on the internal state of
the storage system that is consistency have been reached
as soon as all replica of given data item are the same .How
updates flow through the system and what guarantees the
system be able to provide with respect to update. Here in
this customer does not matter whether or not a storage
system internally contains any stale copies . There is no
stale data is observed from the client point of view the
customer is satisfied. In a client-centric consistency model
focus on what specific customer want that is how the
customer observe data update . It was describes different
level of consistency in distributed system , from strict
consistency to weak Consistency .
High consistency implies high cost and reduced
availability .Client-centric consistency model they do not
care about the internal state of a storage system . They
explained how these two communicate to each other and
introduced an approach which allows to compute the
staleness of data , or how soon ‘eventual’ in eventual
consistency is .

3. PROBLEM STATEMENT
To provide everywhere on access , cloud service
provider maintains numerous replicas for each pieces of
© 2015, IRJET.NET- All Rights Reserved

data on geographically scattered servers. The problem of
using the replication method in cloud is that it is very
expensive to accomplish strong consistency on a
worldwide scale .
Existing solutions can be classified into tracebased verifications [5] and benchmark-based verifications
.Trace-based verifications focus on three consistency
semantics :safety, regularity, and atomicity [5]. Safety :- A
register is safe if a read is not parallel with any write
returns the value of the most recent write , and a read that
is parallel with a write can return any value Regularity :- A
register is regular if a read is not parallel with any write
returns the value of the most recent write , and a read that
is parallel with a write return either the value of the most
recent write ,or the value of the concurrent write
.Atomicity :- A register is atomic if every read returns the
value of the most recent write .

4. PROPOSED SOLUTION
We present a novel consistency as a service
(CaaS) model[1] , where a group of users that constitute
an audit cloud can verify whether the data cloud provides
the promised level of consistency or not.

Fig 2 Consistency as a service model
The Consistency as a service model consists of large data
cloud and various audit cloud .A service level agreement
(SLA) will be busy between the data cloud and the audit
cloud ,which will tell what level of consistency the data
cloud must provide, and how much will be charged if the
data cloud violates the service level agreement .
In User Operation Table Each client maintains a
User Operation Table for recording local operations. Each
record in the User Operation Table is described by three
elements: operation, logical vector, and physical vector.
While issuing an operation, a client will record this

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395 -0056

Volume: 02 Issue: 03 | June-2015

p-ISSN: 2395-0072

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operation, as well as his current logical vector and physical
vector, in his UOT.

At the first level , each client on your own perform
local auditing with his own User operation table . In local
consistency has two types Monotonic-read-consistency
and Read-your-write consistency .
1) Monotonic-read-consistency :- If a process reads
the price of data K , any consecutive read on data
K by that process will always return that same
value or extra recent value .
2) Read-your-write consistency :-The result of a
write by a process on data K will always be a
consecutive read on data K by the similar process.
Monotonic-read consistency requires that a user
should read either a new value or the identical value , and
read-your-write consistency need that a user all the time
read his latest updates
At the second level , an auditor be able to execute
global auditing after obtaining a global trace of all users
operations . global auditing concentrate on Causal
consistency which can be performed by offline algorithm .

Fig. 3. The update process of logical vector and physical
vector. A black solid circle denotes an event
(read/write/send message/receive message), and the
arrows from top to bottom denote the increase of physical
time.
The physical vector is updated in the similar way as
the logical vector, except that the user’s physical clock
keeps growing as time passes, no matter whether an event
(read/write/send message/receive message) happens or
not. The update process is as follows: All clocks are
initialized with zero (for two vectors); The user increases
his own physical clock in the physical vector continuously,
and increases his own logical clock in the logical vector by
one only when an event happens; Two vectors will be sent
along with the message being sent. When a user receives a
message, he updates each element in his vector with the
maximum of the value in his own vector and the value in
the received vector (for two vectors).Each user will
maintain a logical vector and a physical vector to track the
logical and physical time when an operation happens,
resepectively.
A two-level auditing structure, which only requires a
loosely synchronized clock for ordering operations in an
audit cloud. Here each client has to support a logical vector
for limited ordering of operation and implement , a twolevel auditing structure . Each client perform local auditing
separately with a local trace of operation ; periodically an
auditor is selected from the audit cloud to perform global
auditing with global trace of operations.

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1) Causal consistency :- Writes that are causally
related should be seen by all processes in the
similar order. Concurrent writes may be seen in a
dissimilar order on different machines .
Global auditing concentrate on casual consistency,
which is performed by constructing a directed graph . If
the constructed graph is a directed acyclic graph (DAG)
then casual consistency is preserved. Quantify the severity
of violations can be done by two metrics for the CaaS
model: commonality of violations and staleness of the
value of read. Finally it was propose a heuristic auditing
strategy (HAS) which adds appropriate reads to reveal as
several violations as possible .

5. CONCLUSIONS
Consistency as a service (CaaS) model and a twolevel auditing structure to help users validate whether the
cloud service provider (CSP) is providing the promised
consistency and to quantify the severity of the violations is
any . With the CaaS model , the users can assess the quality
of cloud services and select a right cloud service provider
among various candidates , for example the least
expensive one that still provides adequate consistency for
the users application . In future work will determine
dependencies between files on S3. The plan to publish
these result such as bechmarking Apache Cassandra and
the Google App Engine data store to extend our efforts to
additional storage system . Future work , it will conduct a
thorough theoretical study of consistency models in cloud
computing .

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395 -0056

Volume: 02 Issue: 03 | June-2015

p-ISSN: 2395-0072

www.irjet.net

ACKNOWLEDGEMENT
REFERENCES
[1] Qin Liu , Guojun Wang ,Jie Wu, “ Consistency as a
Service : Auditing Cloud Consistency ,” 2014 .
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Wylie, “Toward a principled framework for benchmarking
consistency,” 2012 .
[3] H. Wada, A. Fekete, L. Zhao, K. Lee, and A. Liu, “Data
consistency properties and the trade-offs in commercial
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[4] D. Bermbach and S. Tai, “Eventual consistency: how
soon is eventual?” 2011
[5] E. Anderson, X. Li, M. Shah, J. Tucek, and J. Wylie, “What
consistency does your key-value store actually provide,”
2010
[6] M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A.
Konwinski,G. Lee, D. Patterson, A. Rabkin, I. Stoica, et al., “A
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[7] W. Lloyd, M. Freedman, M. Kaminsky, and D. Andersen,
“Don’t settle for eventual: scalable causal consistency for
wide-area storage with COPS,” in Proc. 2011 ACM SOSP,

BIOGRAPHIES
Ms. Dhanashree.B.Sawale has
completed B.E in Information
Science at Atria Institute of
technology Bangalore , India in
2013. She is currently pursuing
M.Tech in Computer Science
from
Visvesvaraya
Technological University, India.
She is interested with areas of
research related to Cloud
Computing , Image Processing .

Mrs. Resmi.S has completed B.E
(CSE) from Calicut University ,
Kerala
and
M.Tech
(CSE)
Visvesvaraya
Technological
University. She is currently
working with Atria Institute of
Technology, Bangalore as Asst
Professor. She is interested in
Compiler Design , Computer
Organization and Analysis of
Algorithm .

[8] P. Mell and T. Grance, “The NIST definition of cloud
computing (draft),”NIST Special Publication 800-145
(Draft), 2011
[9] S. Esteves, J. Silva, and L. Veiga, “Quality-of-service for
consistency of data geo-replication in cloud computing,”
2012
[10] T. Kraska, M. Hentschel, G. Alonso, and D. Kossmann,
“Consistency rationing in the cloud: pay only when it
matters,” in Proc. 2009

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