Reliability Ranking Prediction for Cloud Services via Skyline
Xiong Wei, Zhao Qi Wu, Bing Li, Bo Hang · 2018
Cloud computing is becoming popular in information technology. The functional properties of cloud services are important, which assure correct functionality of the entire cloud application. Mean-while, the nonfunctional properties such as reliability might significantly influence the user-perceived quality of the application. Thus, building high-reliability cloud applications is a critical research problem. Reliability rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. There existed several methods that can conduct reliability ranking prediction of cloud services. However, those methods did not resolve Skyline issue well, which is difficult to be ranked. This paper proposes an approach to reliability ranking prediction for cloud services via Skyline on the past service usage experiences of other consumers. It avoids expensive and time-consuming Web service invocations. To validate our approach, large-scale experiments are conducted based on a real-world Web service dataset, WSDream. The results show that our proposed approach achieves higher prediction accuracy than other approaches.