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Providing Ranks to Services in Cloud Computing Environment Based on Quality of Service V Mounika Iashwarya & B Mallikarjuna

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ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

ABSTRACT:

Cloud computing is an Internet-based computing mod-el. This model enables accessing to information re-sources in request time. Cloud computing users always

have applications with different requirements. On the other hand, there are different cloud Service providers which present services with different qualitative char -acteristics.

Determining the best cloud computing service for

a specific application is a serious problem for users. Ranking compares the different services offered by dif -ferent providers based on quality of services, in order to select the most appropriate service. In this paper, the existing approaches for ranking cloud computing services are analyzed. The overall performance of each method is presented by reviewing and comparing of

them. Finally, the essential features of an efficient rat -ing system are indicated.

QoS is an important research topic in cloud comput -ing. It helps users in making decision on optimal cloud service selection from no. of functionality equivalent

services. QoS values of cloud services provide valuable

information to assist decision making. In cloud applica-tions, cloud services are invoked remotely by internet connections, where as in traditional component based systems software components are invoked locally. Cli-ent side performance of cloud services is thus greatly

influenced by internet connections .

Therefore different cloud applications may receive dif

-ferent levels of quality for same cloud service. Other words the QoS ranking of cloud services for a user

cannot be transferred directly for another user, since

the location of the cloud applications are quite differ -ent. Personalized cloud services Qos ranking is thus

re-V.Mounika Iashwarya

Student M.tech, Department of CSE, School of Technology,

GITAM University.

B.Mallikarjuna

Assistant Professor, Department of CSE, School of Technology,

GITAM University.

This approach of personalized cloud services QoS rank -ing is to evaluate all the candidate services at the user

side and rank the services based on the observed QoS

values. However, this approach is impractical in reality, since invocations of cloud services may be charged. Even if the invocations are free, executing a large num-ber of service invocations is time consuming and re-source consuming, and some service invocations may

produce irreversible effects in the real world. More -over, when the number of candidate service is large, it

is difficult for the cloud application.

Keywords:

Cloud Computing, Cloud Service Provider, Quality of Service, Ranki

1. INTRODUCTION:

Cloud Computing model provides services and deliv-ers on demand resources (such as software, platform and infrastructure [1, 2]) in user’s request time. This computational model has been developed like other utilities of water, electricity and gas; it can be consid-ered as the next utility required for human. In this en-vironment each user has its own unique requirement.

Thus, selecting the best service that fulfills user’s ap -plication requirements is an important research chal-lenge [3]. The usage of service usually determines the success of its application infrastructure. The provider’s capability cannot be fully utilized by selecting wrong

services [4-6]. The quality of service (QoS) information

is required in service comparison. This information can

be measured by providers or a third party [5]. Some

attributes like response time, delay, usability, security,

privacy and availability are defined for preparing qual -ity of service information. The value of these attributes represents degree of quality of services [6, 7].

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ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

Generally, the goal of ranking of services is helping

users to evaluate and compare different services. So,

users can select the most appropriate service that

sat-isfies their requirement. This paper describes service

rankings in two parts. Firs part treat evaluation and comparison of services and the second part treat

ser-vice ranking. Serser-vice ranking and selecting most appro -priate service is performed by some approaches, such

as three component architecture of Service Measure

-ment Index (SMI) Cloud [7], service mapper [8], Service Ranking System (SRS) [9], SLA Matching [10], Cloud

-Rank [11] and Aggregation [12]. All of these approaches have their own advantages and limitations. Mention -ing their advantages and disadvantages or limitations

is useful for preparing an efficient ranking system.The

remainder of this paper is as follows: in section 2, basic and background information for ranking is presented.

In section 3, different service ranking approaches are

reviewed in detail. In section 4, we compare current

approaches and finally, section 5 draws some conclu -sion.

2. RANKING AND BACKGROUND:

In this section, concepts of ranking, system monitoring

and quality of services are defined. Quality attributes

which are used in service comparison are introduced at the end of this section.

2.1 Ranking:

Generally, ranking is sorting and assigning a degree to some choices. This concept is applied in some cases, such as ranking of universities and web services and another where [13]. But, applying it in rank assignment to cloud services is a new concept which draws some attentions in recent years. In cloud computing

environ-ment, ranking differs from other systems because of

existing infrastructure. This infrastructure is

connect-ing different components by means of Internet and

internet connections are unpredictable [14]. Therefore

in cloud environment, maybe different level of quality of service [15] received by different users but for same cloud service. So it is required that a ranking system receives user’s requests with different requirement levels. Then, it finds some services which satisfy user

requirements and ranks them for each user based on

QOS. A framework is needed to perform these tasks.

This framework should have the ability of receiving information from users and selecting the best service based on their requirements by service monitoring.

Also, it is needed to consider following items for rank -ing of selected services [16, 17]. systems, software and applications.

Fig No:1

1.2 Deployment of Cloud Services:

Cloud services are typically made available via a private cloud, community cloud, public cloud or hybrid cloud, Generally speaking services provided by the public

cloud are offered over the internet and are owned and

operated by a cloud provider .some examples include services aimed at the general public, such as online photo storage services, e-mail services, or social net-working sites. However, services for enterprises can

also be offered in a public cloud. In a private cloud, the cloud infrastructure is operated solely for a specific

organization, and is managed by the organization or a third party .In a community cloud, the service is shared by several organizations and made available only to those groups. The infrastructure may be owned and operated by the organization or by a cloud service pro-viderCloud computing has many advantages like we can easily upload &download data stored in cloud. We can access data from anywhere, any time on demand.

Cost efficient, hard ware and soft ware resources are

easily available and is location independent. The major advantage is security. Cloud application are typically large scale and complex, popularity of cloud

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ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

Cloud applications involve multiple cloud components which communicate with each other, over applica-tion programming interfaces. Fig[2] shows example of cloud applications. Cloud application1 is tourism

web-site deployed in cloud. (eg: Amazon Ec2 http://aws. amazon.com/ec2), providing various tourism services. Each service provided will fulfill a specified functional -ity.

Airplane ticket services, car rental services and hotel booking services are application services provided fig[

]. These cloud services can also be employed by other

cloud application. Since these are number of function -ality equivalent services in the cloud optimal service se-lection becomes important. In this paper, service users refer to cloud applications that use cloud services. Non functional performance of cloud services is usually

de-scribed by quality of service (QOS).

QoS is an important research topic in cloud comput -ing. It helps users in making decision on optimal cloud service selection from no. of functionality equivalent

services. QoS values of cloud services provide valuable

information to assist decision making. In cloud applica-tions, cloud services are invoked remotely by internet connections, where as in traditional component based systems software components are invoked locally.

Client side performance of cloud services is thus

great-ly influenced by internet connections .Therefore differ

-ent cloud applications may receive differ-ent levels of quality for same cloud service. Other words the QoS

ranking of cloud services for a user cannot be trans-ferred directly for another user, since the location of

the cloud applications are quite different. Personalized cloud services Qos ranking is thus required for differ -ent cloud applications.

This approach of personalized cloud services QoS rank -ing is to evaluate all the candidate services at the user

side and rank the services based on the observed QoS

values. However, this approach is impractical in reality, since invocations of cloud services may be charged. Even if the invocations are free, executing a large num-ber of service invocations is time consuming and re-source consuming, and some service invocations may

produce irreversible effects in the real world. More -over, when the number of candidate service is large, it

is difficult for the cloud application, designers to evalu

-Fig No:2

To attack this challenge, thus paper proposes a

person-alized ranking prediction framework called “CLOUD RANK”. This predicts the Qos ranking of cloud service without requiring. Additional real world service invoca -tions from intended users. This paper takes advantage of past usage experiences of other users to make per-sonalized ranking prediction for current users. The con-tribution of this paper includes:

•This paper identifies problem and requirement pres

-ent in Qos ranking of cloud services and proposes. A

personalized Qos ranking prediction framework called

CLOUD RANK. As for as know cloud rank is first per -sonalized Qos ranking prediction framework for cloud services.

•Real world experiments are conducted to study ac -curacy in ranking prediction algorithms compared with other competing ranking algorithms. The experimental results show accuracy

2. LITURATURE SARVEY:

All over world there are some research papers in Qos, ranking of cloud, collaborates filtering etc. surveying

those papers, useful information for developing a per-sonalized prediction is gathered.

2.1 Qos for Web Service Recommendation Is

(By Collaborative Filtering)[15]

:

Qos is the main for analyzing non functional

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ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

For predicting Qos values of web services. So that it

makes recommendation for best web service. This process is done by taking advantages of past usage

experiences of service users a collaborative filtering

approach is designed based on the collected data of Qos by past usages. The total concept is designed to give ranking to which services. This mechanism is taken into analyzation for creating personalized ranking pre-diction frame work for cloud service.

2.2 Finding Similarity of User Feedback[9]

Number of users gives different types of feedbacks to

give a identical information for an user about Qos of a

service. Similarity between various types of feedback

should be analyzed. This similarity of feed backs gives users a identical information for decision making. For example a service that is highly favored by one user,

may not have same impact on other user. So similarity

is required here and optimization algorithm is made to compute similarity. This idea is adopted for creating a personalized ranking prediction.

2.3 Data Base for User Feedbacks

Empirical analyses of predictive algorithm for

collab-orative filtering. Says paper keeps a data base about

user performance to predict users like, they compare accuracy of various methods in a set of representative

problem domains. Algorithm estimates utility of ranked

list of suggested items. This estimates probability that a user will see recommendation or feedback given to a service.

2.4 Performance Analysis Of Create

Commer-cial Cloud:

Performance analysis of cloud computing services for

many tasks scientific computing.Creating a real time cloud for each and every scientific experiment is diffi -cult. Every company and institute cannot have

expen-sive computing facilities. So commercial clouds are created (Amazon EC2 is a largest commercial cloud).

These commercial clouds serve single set of physical

resources a large user base with different needs. They

satisfy owners economically and become alternative for scientists.

They also support web and small database workloads.

They serve many tasks computing (MTC) here they analyze performance of commercial cloud services. So that improvements can be made to settle difference between offer and demand. This work is taken into

consideration for creating a personalized ranking pre-diction.

2.5 Finding Similar Item Based Recommended

Information

Item based Toppan recommendation algorithmA per

-sonalized information filtering technology is needed to find recommended. System even in www and Ecom

-merce user. Based collaborative filtering is used ev

-erywhere for finding recommended systems. But the computational complexity. Of this method various

with number of customers. When there are millions of users, model based. Recommendation techniques are developed to address this problem. This computes list of recommendations and determines similarity

be-tween various items and then identifies set of items to

be recommended. This model of computing is taken into consideration for creating a personalized ranking prediction.

2.6 Learning to Order Things:

The procedure of ordering given feedback in form of preference judgment is shown. Items having one

in-stance ranking should be ordered first then others. Or -dering preference should be given by consi-dering the ranking provided by them. This process is considered to create personalized ranking prediction.

3. PROPOSED SYSTEM:

•In this paper, we propose a personalized ranking pre

-diction framework, named CLOUD RANK. To predict

this Qos ranking a set of cloud services without requir-ing users in decision makrequir-ing about which cloud is go-ing to adopt and which cloud application present these days.

•For making this personalized ranking prediction for

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ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

•This paper identifies critical problem of cloud services

and proposes a Qos ranking prediction framework to address a problem. It shows the user the information about level of Qos provided.

•CLOUD RANK is the first personalized Qos ranking

prediction framework for cloud services. Which would help user in adopting best cloud application services?

4. MODULES:

There are tree modules in this paper.

•Authentication

•User privileges

•Cloud provider

Authentication:

Only after authentication, user becomes an authorized one. Only a authorized user can use services.

User Privileges:

An authorized user can view services provided by cloud

application, or a past user can give Qos ranking for cloud applications.

Cloud Provider:

The feedback provided by various users, won’t be same

about different cloud application. Cloud provider anal -yses feedback and calculates similarity between user feedbacks and provide Qos ranking for each and every cloud application.

ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

5. REFERENCES:

[1] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A. Konwinski, G. Lee, D.A. Patterson, A. Rabkin, I. Storica, and M. Zaharia, “Above the clouds: ABerkeley view of cloud computing, “Technical report EECS-2009-28, Univ. California, Berkeley, 2009.

[2] K.J. arvelin and J. Kekalaninen, “Cumulated Gain-Based Evaluation of IR Techniques” ACM Trans. Infor

-mation system vol. 20, no.4, pp. 422-446, 2002.

[3] P.A. Bonatti and P. Festa, “On Optimal Service Selec

-tion” proc. 14th Int’I Conf. World Wide Web (WWW’05), pp. 530-538,2005.

[4] J.S Breese, D. Heckerman, and C. Kadie, “Empiri

-cal Analysis of predictive algorithms for collaborative filtering “Proc. 14th Ann. Conf. Uncertainty in Artificial Intelligence (UAI ’98), pp. 43-52, 1998.

[5] R. Burke, “Hybrid Recommender Systems: Survey and Experiments, “User Modeling and User- Adapted Interaction, vol. 12, no. 4, pp. 331-370, 2002

[6] W.W. Cohen, R.E. Schapire, and y. Singer, “Learning to Order things” J. Artificial intelligent Research, vol. 10, no. 1, pp. 243-270, 1999.

[7] M. Deshpande and G. Karypis, “item-Based Top-n Recommendation” ACM Trans. Information System, vol. 22, no. 1, pp. 143-177, 2004.

[8] A. Iosup, S. Ostermann, N. Yigitbasi, R. Prodon, T. Fahringer, and D. Epema, “Performance Analysis of cloud computing services for many-tasks scientific computing” IEEE trans. Parallel distributed systems, vol. 22, no. 6, pp. 931-945, june 2011.

[9] R. Jin, J.Y. Chai, and L. Si,”An Automatic weighting Scheme for collaborative Filtering” proc. 27th Int’I ACM SIGIR Conf. Research and Development in information Retrieval (SIGIR’04), pp. 337-344, 2004.

[10] H.Khazaei, J. Misic, and V.B Misic, “Performance Analysis of Cloud Computing centers using m/g/m/m+r Queueing systems” IEEE trans. Parallel distributed sys

(6)

ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

•This paper identifies critical problem of cloud services

and proposes a Qos ranking prediction framework to address a problem. It shows the user the information about level of Qos provided.

•CLOUD RANK is the first personalized Qos ranking

prediction framework for cloud services. Which would help user in adopting best cloud application services?

4. MODULES:

There are tree modules in this paper.

•Authentication

•User privileges

•Cloud provider

Authentication:

Only after authentication, user becomes an authorized one. Only a authorized user can use services.

User Privileges:

An authorized user can view services provided by cloud

application, or a past user can give Qos ranking for cloud applications.

Cloud Provider:

The feedback provided by various users, won’t be same

about different cloud application. Cloud provider anal -yses feedback and calculates similarity between user feedbacks and provide Qos ranking for each and every cloud application.

ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

5. REFERENCES:

[1] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A. Konwinski, G. Lee, D.A. Patterson, A. Rabkin, I. Storica, and M. Zaharia, “Above the clouds: ABerkeley view of cloud computing, “Technical report EECS-2009-28, Univ. California, Berkeley, 2009.

[2] K.J. arvelin and J. Kekalaninen, “Cumulated Gain-Based Evaluation of IR Techniques” ACM Trans. Infor

-mation system vol. 20, no.4, pp. 422-446, 2002.

[3] P.A. Bonatti and P. Festa, “On Optimal Service Selec

-tion” proc. 14th Int’I Conf. World Wide Web (WWW’05), pp. 530-538,2005.

[4] J.S Breese, D. Heckerman, and C. Kadie, “Empiri

-cal Analysis of predictive algorithms for collaborative filtering “Proc. 14th Ann. Conf. Uncertainty in Artificial Intelligence (UAI ’98), pp. 43-52, 1998.

[5] R. Burke, “Hybrid Recommender Systems: Survey and Experiments, “User Modeling and User- Adapted Interaction, vol. 12, no. 4, pp. 331-370, 2002

[6] W.W. Cohen, R.E. Schapire, and y. Singer, “Learning to Order things” J. Artificial intelligent Research, vol. 10, no. 1, pp. 243-270, 1999.

[7] M. Deshpande and G. Karypis, “item-Based Top-n Recommendation” ACM Trans. Information System, vol. 22, no. 1, pp. 143-177, 2004.

[8] A. Iosup, S. Ostermann, N. Yigitbasi, R. Prodon, T. Fahringer, and D. Epema, “Performance Analysis of cloud computing services for many-tasks scientific computing” IEEE trans. Parallel distributed systems, vol. 22, no. 6, pp. 931-945, june 2011.

[9] R. Jin, J.Y. Chai, and L. Si,”An Automatic weighting Scheme for collaborative Filtering” proc. 27th Int’I ACM SIGIR Conf. Research and Development in information Retrieval (SIGIR’04), pp. 337-344, 2004.

[10] H.Khazaei, J. Misic, and V.B Misic, “Performance Analysis of Cloud Computing centers using m/g/m/m+r Queueing systems” IEEE trans. Parallel distributed sys

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ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

A Peer Reviewed Open Access International Journal

[11] G. Linden, B. Smith, and J. York, “Amazon.Com Rec

-ommendations: Item-to-item Collaborative filtering” IEEE internet computing, vol. 7, no. 1, pp. 76-80, jan./ Feb. 2003.

[12] N.N. Liu and Q. Yang, “Eigenrank: A Ranking-Ori

-ented Approach to Collaborative Filtering” proc. 31st Int’I ACM SIGIR Conf. Research and development in In

-formation Retrieval(SIGIR ’08), pp. 83-90,2008.

[13] J. Marden, analyzing and Modeling Ranking Data. Chapman & Hall, 1995.

[14] T. Yu, Y. Zhang, and K. –j. Lin, “Efficient Algorithms

for web services selection with end-to-end Qos

con-straints” ACM trans. Web, vol. 1, no. 1, pp. 1-26,2007.

[15] Z. Zheng, H. Ma, M.R. Lyu, and I. King “Qos- Aware Web service Recommendation by collaborative filter

-ing” IEEE trans. Service computing, vol. 4, no. 2, pp. 140-152, Apr-june 2011.

[16] Z.Zheng, H.Ma, M.R. Lyu, and I. King, “WSRec: A Col

-laborative Filtering Based Web Service Recommender system” proc. Seventh Int’I Conf. Web Services(ICWS ’09), pp. 437-444, 2009.

ISSN No: 2348-4845

International Journal & Magazine of Engineering,

Technology, Management and Research

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