DocumentCode :
50361
Title :
QoS Ranking Prediction for Cloud Services
Author :
Zibin Zheng ; Xinmiao Wu ; Yilei Zhang ; Lyu, Michael R. ; Jianmin Wang
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, China
Volume :
24
Issue :
6
fYear :
2013
fDate :
Jun-13
Firstpage :
1213
Lastpage :
1222
Abstract :
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.
Keywords :
Web services; cloud computing; quality of service; QoS ranking prediction framework; Web services; cloud application; cloud computing; cloud service selection; quality of service; service usage experience; Accuracy; Cloud computing; Educational institutions; Indexes; Prediction algorithms; Quality of service; Quality-of-service; cloud service; personalization; ranking prediction;
fLanguage :
English
Journal_Title :
Parallel and Distributed Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9219
Type :
jour
DOI :
10.1109/TPDS.2012.285
Filename :
6320550
Link To Document :
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