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Many enterprises have a growing interest in service composition to construct their business applications. With the increase of alternative services, Quality of Service (QoS) becomes an important indicator of obtaining optimal composite services. Due to the dynamic nature of the service environment, a composite service may not guarantee to deliver an overall optimal QoS. Re-optimization approaches...
In the era of big data, data intensive applications have posed new challenges to the filed of service composition, i.e. composition efficiency and scalability. How to compose massive and evolving services in such dynamic scenarios is a vital problem demanding prompt solutions. As a consequence, we propose a new model for large-scale adaptive service composition in this paper. This model integrates...
This paper describes a multi-agent reinforcement learning model for the optimization of Web service composition. Based on the model, we propose a multiagent Q-learning algorithm, where each agent would benefit from the advice of other agents in team. In contrast to single-agent reinforcement learning, our algorithm can speed up convergence to optimal policy. In addition, it allows composite service...
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