QoS Ranking Prediction for Cloud Services

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...

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Bibliographic Details
Published inIEEE transactions on parallel and distributed systems Vol. 24; no. 6; pp. 1213 - 1222
Main Authors Zibin Zheng, Xinmiao Wu, Yilei Zhang, Lyu, Michael R., Jianmin Wang
Format Journal Article
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Accuracy
Cloud computing
cloud service
Educational institutions
Indexes
personalization
Prediction algorithms
Quality of service
ranking prediction
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Summary: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.
ISSN:1045-9219
DOI:10.1109/TPDS.2012.285