Large-Scale and Adaptive Service Composition Using Deep Reinforcement Learning

Abstract

Service composition provides an effective way to implement a Service-Oriented Architecture (SOA) by combining existing multiple services to meet user requirements. The increasingly complex user requirements and large amount of services pose a significant challenge to service selection and composition. Furthermore, web services are network based, which are inherently dynamic. The environment of service composition may also be complex and unstable. These demand a service composition solution to adapt to the change of environment. In this paper, we propose a new service composition solution based on Deep Reinforcement Learning (DRL) for adaptive and large-scale service composition problems. The experimental results demonstrate the effectiveness, scalability and self-adaptivity of our approach.