The Community for Technology Leaders
RSS Icon
Issue No.06 - June (2013 vol.24)
pp: 1213-1222
Zibin Zheng , The Chinese University of Hong Kong, Hong Kong and Beijing University of Posts and Telecommunications, Beijing
Xinmiao Wu , Sun Yat-sen University, Guangzhou
Yilei Zhang , The Chinese University of Hong Kong, Hong Kong
Michael R. Lyu , The Chinese University of Hong Kong, Hong Kong
Jianmin Wang , Sun Yat-sen University, Guangzhou
Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The experimental results show that our approaches outperform other competing approaches.
Quality of service, Cloud computing, Prediction algorithms, Educational institutions, Accuracy, Indexes, personalization, Quality-of-service, cloud service, ranking prediction
Zibin Zheng, Xinmiao Wu, Yilei Zhang, Michael R. Lyu, Jianmin Wang, "QoS Ranking Prediction for Cloud Services", IEEE Transactions on Parallel & Distributed Systems, vol.24, no. 6, pp. 1213-1222, June 2013, doi:10.1109/TPDS.2012.285
[1] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A. Konwinski, G. Lee, D.A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, "Above the Clouds: A Berkeley View of Cloud Computing," Technical Report EECS-2009-28, Univ. California, Berkeley, 2009.
[2] K.J. arvelin and J. Kekalainen, "Cumulated Gain-Based Evaluation of IR Techniques," ACM Trans. Information Systems, vol. 20, no. 4, pp. 422-446, 2002.
[3] P.A. Bonatti and P. Festa, "On Optimal Service Selection," Proc. 14th Int'l Conf. World Wide Web (WWW '05), pp. 530-538, 2005.
[4] J.S. Breese, D. Heckerman, and C. Kadie, "Empirical 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. Prodan, T. Fahringer, and D. Epema, "Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing," IEEE Trans. Parallel Distributed System, 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'l 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 Queuing Systems," IEEE Trans. Parallel Distributed System, vol. 23, no. 5, pp. 936-943, May 2012.
[11] 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, Jan./Feb. 2003.
[12] N.N. Liu and Q. Yang, "Eigenrank: A Ranking-Oriented Approach to Collaborative Filtering," Proc. 31st Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '08), pp. 83-90, 2008.
[13] H. Ma, I. King, and M.R. Lyu, "Effective Missing Data Prediction for Collaborative Filtering," Proc. 30th Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '07), pp. 39-46, 2007.
[14] J. Marden, Analyzing and Modeling Ranking Data. Chapman & Hall, 1995.
[15] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "Grouplens: An Open Architecture for Collaborative Filtering of Netnews," Proc. ACM Conf. Computer Supported Cooperative Work, pp. 175-186, 1994.
[16] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-Based Collaborative Filtering Recommendation Algorithms," Proc. 10th Int'l Conf. World Wide Web (WWW '01), pp. 285-295, 2001.
[17] J. Wu, L. Chen, Y. Feng, Z. Zheng, M. Zhou, and Z. Wu, "Predicting QoS for Service Selection by Neighborhood-Based Collaborative Filtering," IEEE Trans. System, Man, and Cybernetics, Part A, to appear.
[18] C. Yang, B. Wei, J. Wu, Y. Zhang, and L. Zhang, "Cares: A Ranking-Oriented Cadal Recommender System," Proc. Ninth ACM/IEEE-CS Joint Conf. Digital Libraries (JCDL '09), pp. 203-212, 2009.
[19] T. Yu, Y. Zhang, and K.-J. Lin, "Efficient Algorithms for Web Services Selection with End-to-End QoS Constraints," ACM Trans. Web, vol. 1, no. 1, pp. 1-26, 2007.
[20] L. Zeng, B. Benatallah, A.H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, "QoS-Aware Middleware for Web Services Composition," IEEE Trans. Software Eng., vol. 30, no. 5, pp. 311-327, May 2004.
[21] Z. Zheng and M.R. Lyu, "WS-DREAM: A Distributed Reliability Assessment Mechanism for Web Services," Proc. 38th Int'l Conf. Dependable Systems and Networks (DSN '08), pp. 392-397, 2008.
[22] Z. Zheng, H. Ma, M.R. Lyu, and I. King, "WSRec: A Collaborative Filtering Based Web Service Recommender System," Proc. Seventh Int'l Conf. Web Services (ICWS '09), pp. 437-444, 2009.
[23] Z. Zheng, H. Ma, M.R. Lyu, and I. King, "QoS-Aware Web Service Recommendation by Collaborative Filtering," IEEE Trans. Service Computing, vol. 4, no. 2, pp. 140-152, Apr.-June 2011.
[24] Z. Zheng, Y. Zhang, and M.R. Lyu, "CloudRank: A QoS-Driven Component Ranking Framework for Cloud Computing," Proc. Int'l Symp. Reliable Distributed Systems (SRDS '10), pp. 184-193, 2010.
19 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool