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.
Highlights
A Service-Oriented-Architecture (SOA) is a type of application architecture in which all services/functions are defined by a description language and possess interfaces that are invoked to perform business processes
We develop predictive service composition schedules based on an ARIMA time-series prediction model
In this work, we develop a predictive web service composition approach aiming at capturing run-time fluctuations of service quality and leveraging its trend information in guiding QoS-aware service composition
Summary
A Service-Oriented-Architecture (SOA) is a type of application architecture in which all services/functions are defined by a description language and possess interfaces that are invoked to perform business processes. Time-varying QoS of services strongly affect user-perceived performance of resulting composite services. Such variations of services may result in a less desired outcome in terms of increased physical resources and backup services. It is evident that run-time QoS fluctuations and QoS trends of component services should be well considered and predicted in service composition process. We deal with the fluctuation issue of QoS in service composition process To solve this problem, we propose a predictive service composition approach with special attention to time-varying QoS. We exploit an ARIMA-based time-series prediction model to analyze the QoS fluctuations and GA to generate optimal composite service where ARIMA stands for Auto-Rregressive-IntegratedMoving-Average proposed by George and Jenkins [12]. The results show that our predictive service composition algorithm outperforms existing ones
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