Grey Weighted QoS Evaluation based on Real-Time Scene

doi:10.23940/ijpe.20.04.p8.569576

Abstract

Abstract:

Service evaluation algorithm based on QoS can help users quickly select the best one from mass services to meet their needs. However, the existing service evaluation algorithm rarely considers real-time problems such as users' real-time scenario and QoS volatility. To solve this problem, a grey weighted QoS evaluation algorithm based on real-time scene is proposed. The algorithm is divided into three stages: In the first stage, the environment matching algorithm is used to quickly reject the services that cannot be successfully called due to the current user environment; in the second stage, the real-time dynamic attribute QoS value of candidate services is corrected; in the third stage, the comprehensive weight combined by AHP and entropy weighting method is used to weight the correlation coefficient generated by the grey comprehensive evaluation algorithm, and the service with maximum correlation degree is the optimal selection. The algorithm is tested on QWS dataset, and its feasibility and validity are verified by analysis.

Key words: QoS, real-time scene, volatility, relative index correction, grey weighted evaluation

Xiulian Tang and Guoqiang Zhou. Grey Weighted QoS Evaluation based on Real-Time Scene [J]. Int J Performability Eng, 2020, 16(4): 569-576.

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