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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
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