Predictive-Trend-Aware Composition of Web Services with Time-Varying Quality-of-Service

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Abstract

Service composition is a technology capable of combing a collection of existing services where many smaller services are coordinated together to form a larger one. Functionally similar services can often show different quality-of-service (QoS) properties. For a specific service composition request, how to choose from a bag of suitable services that fulfill the required functions under given quality-of-service constraints is widely believed to be a great challenge. The traditional approach usually tackles this problem by assuming fixed, bounded, or statistic QoS and views the decision-making of service composition as a static process. Instead, we address this problem by considering time-varying and fluctuating QoS and presenting a predictive-trend-aware service composition method by using a time series prediction model and genetic algorithms. We conduct extensive case studies based on multiple randomly-generated service templates with varying process configurations and show that our method outperforms existing ones.

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Sun, X., Wang, S., Xia, Y., & Zheng, W. (2020). Predictive-Trend-Aware Composition of Web Services with Time-Varying Quality-of-Service. IEEE Access, 8, 1910–1921. https://doi.org/10.1109/ACCESS.2019.2962703

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