QoS Ranking Prediction for Cloud Services
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AbstractCloud 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.
All Author(s) ListZheng ZB, Wu XM, Zhang YL, Lyu MR, Wang JM
Journal nameIEEE Transactions on Parallel and Distributed Systems
Volume Number24
Issue Number6
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1213 - 1222
LanguagesEnglish-United Kingdom
Keywordscloud service; personalization; Quality-of-service; ranking prediction
Web of Science Subject CategoriesComputer Science; Computer Science, Theory & Methods; COMPUTER SCIENCE, THEORY & METHODS; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

Last updated on 2020-17-10 at 01:22