Open Access   Article Go Back

Ranking Prediction for Cloud Services from The Past Usages

G.P. Kumar1 , K. Morarjee2

Section:Research Paper, Product Type: Journal Paper
Volume-2 , Issue-9 , Page no. 22-25, Sep-2014

Online published on Oct 04, 2014

Copyright © G.P. Kumar, K. Morarjee . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: G.P. Kumar, K. Morarjee , “Ranking Prediction for Cloud Services from The Past Usages,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.22-25, 2014.

MLA Style Citation: G.P. Kumar, K. Morarjee "Ranking Prediction for Cloud Services from The Past Usages." International Journal of Computer Sciences and Engineering 2.9 (2014): 22-25.

APA Style Citation: G.P. Kumar, K. Morarjee , (2014). Ranking Prediction for Cloud Services from The Past Usages. International Journal of Computer Sciences and Engineering, 2(9), 22-25.

BibTex Style Citation:
@article{Kumar_2014,
author = {G.P. Kumar, K. Morarjee },
title = {Ranking Prediction for Cloud Services from The Past Usages},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2014},
volume = {2},
Issue = {9},
month = {9},
year = {2014},
issn = {2347-2693},
pages = {22-25},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=247},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=247
TI - Ranking Prediction for Cloud Services from The Past Usages
T2 - International Journal of Computer Sciences and Engineering
AU - G.P. Kumar, K. Morarjee
PY - 2014
DA - 2014/10/04
PB - IJCSE, Indore, INDIA
SP - 22-25
IS - 9
VL - 2
SN - 2347-2693
ER -

VIEWS PDF XML
3841 3625 downloads 3606 downloads
  
  
           

Abstract

Web services are loosely-coupled software systems considered hold up interoperable machine-to-machine communication over a system. The most undemanding approach personalized cloud service quality of service ranking is to assess the entire service candidates at user side and position services base on observed values of quality of service. The materialization of web services has produces unprecedented prospect for organizations to setup additional agile as well as versatile collaborations with other organizations. Comparable to established component-based systems, cloud applications normally entail numerous cloud components that communicate over application programming interface. To attack this crucial challenge, we put forward a personalized ranking prediction structure, named cloud Rank to forecast quality of service ranking concerning a set of cloud services devoid of requiring extra real-world service invocations from the projected users. The target users of cloud rank structure are cloud applications, which require personalized cloud service ranking in support of building selection of optimal service.

Key-Words / Index Term

Cloud Service, Quality Of Service, Cloud Rank, Personalized Service

References

[1] G. Linden, B. Smith, and J. York, �Amazon.com recommendations: Item-to-item collaborative filtering,� IEEE Internet Computing, vol. 7, no. 1, pp. 76�80, 2003.
[2] N. N. Liu and Q. Yang, �Eigenrank: a ranking-oriented approach to collaborative filtering,� in Proc. 31st Int�l ACM SIGIR Conf. On Research and Development in Information Retrieval (SIGIR�08), 2008, pp. 83�90.
[3] H. Ma, I. King, and M. R. Lyu, �Effective missing data prediction for collaborative filtering,� in Proc. 30th Int�l ACM SIGIR Conf. On Research and Development in Information Retrieval (SIGIR�07), 2007, pp. 39�46.
[4] J. Marden, Analyzing and Modeling Ranking Data. Chapman & Hall, New York, 1995.
[5] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, �Grouplens: An open architecture for collaborative filtering of netnews,� in Proc. of ACM Conf. Computer Supported Cooperative Work, 1994, pp. 175�186.
[6] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, �Item-based collaborative filtering recommendation algorithms,� in Proc. 10th Int�l Conf. World Wide Web (WWW�01), 2001, pp. 285�295.
[7] J. Wu, L. Chen, Y. Feng, Z. Zheng, M. Zhou, and Z. Wu, �Predicting qos for service selection by neighborhood-based collaborative filtering,� IEEE Transactions on System, Man, and Cybernetics, Part A, to appear.
[8] C. Yang, B. Wei, J. Wu, Y. Zhang, and L. Zhang, �Cares: a rankingoriented cadal recommender system,� in Proc. 9th ACM/IEEE-CS joint conference on Digital libraries (JCDL�09), 2009, pp. 203�212.
[9] T. Yu, Y. Zhang, and K.-J. Lin, �Efficient algorithms for Web services selection with end-to-end QoS constraints,� ACM Trans. the Web, vol. 1, no. 1, pp. 1�26, 2007.
[10] L. Zeng, B. Benatallah, A. H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, �QoS-aware middleware for Web services composition,� IEEE Trans. Software Engineering, vol. 30, no. 5, pp. 311�327, 2004.
[11] Z. Zheng and M. R. Lyu, �WS-DREAM: A distributed reliability assessment mechanism for Web services,� in Proc. 38th Int�l Conf. Dependable Systems and Networks (DSN�08), 2008, pp. 392�397.
[12] Z. Zheng, H. Ma, M. R. Lyu, and I. King, �WSRec: A collaborative filtering based Web service recommender system,� in Proc. 7th Int�l Conf. Web Services (ICWS�09), 2009, pp. 437�444.