QoS prediction method for data supply chain based on context

Abstract

Due to the execution paradigm may be different at different invocation time, users obtain different QoS when interacting with the same Data Supply Chain (DSC). However, existing QoS prediction methods seldom took this observation into consideration, which shall decrease the prediction accuracy. In this paper, we propose a context-based QoS prediction method for data supply chain. First, a QoS mathematical model is developed for considering the mass data transmission across elementary sub-chains. Then, two execution paradigms of data supply chain are discussed. Besides, we explored several special context factors of data supply chain (such as invocation time, data source update period and execution paradigm) which influence QoS. By processing such context information, we can obtain the part of data supply chain which is need to execute when the user query occurs and leverage them to predict QoS. Experimental results indicate that our approach improves the prediction accuracy and efficiency of QoS when compared to previous methods.