持续自适应的Web服务组合方法

Web Service Composition Method with Continual Self-adaptability

针对基于过程组合方法对Web环境缺乏持续适应性的问题,将强化学习机制应用于该类服务组合,提出一种持续自适应的服务组合算法.该算法将对现有Web服务性能数据的利用与对新服务组合持续的探索相结合,根据服务的实际QoS性能,逐渐逼近与过程模型相对应的优化服务组合策略.结果表明,该学习算法对Web环境的适应具有可连续性,可在每次运行时不仅能感知Web服务及其性能的变化,而且还能利用以往算法执行所获得的Web服务性能数据,调整服务组合策略.通过对熵取值范围的讨论,阐明了对以往策略的利用与持续探索之间的关系;通过在静态和动态两种环境下的实验,验证了算法对环境的适应能力.

The reinforcement learning mechanism was applied to the process-based Web service composition and the Web service composition algorithm with continual self-adaptability was proposed herein so as to enable continuous adaptation to the dynamic Web environment. The algorithm integrates the exploitation of past data about the Web service performance with the continual exploration of new options according to QoS actual performance and approaches the optimal Web service composition policy corresponding to the process model gradually. Compared with other similar methods, the algorithm can adjust the Web service composition solution so as to adapt to the dynamic Web continually by means of perceiving the change of Web service and its performance on the Web, and exploiting the acquired data about the past performance of individual services at any runtime. The relation between exploitation and exploration is accounted for by discussing the range in which the entropy takes its values. Two kinds of experiments were performed to verify the algorithm＇ s adaptability to the static and dynamic environment.