Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition

Abstract

Service-oriented architecture is a widely used software engineering paradigm to cope with complexity and dynamics in enterprise applications. Service composition, which provides a cost-effective way to implement software systems, has attracted significant attention from both industry and research communities. As online services may keep evolving over time and thus lead to a highly dynamic environment, service composition must be self-adaptive to tackle uninformed behavior during the evolution of services. In addition, service composition should also maintain high efficiency for large-scale services, which are common for enterprise applications. This article presents a new model for large-scale adaptive service composition based on multi-agent reinforcement learning. The model integrates reinforcement learning and game theory, where the former is to achieve adaptation in a highly dynamic environment and the latter is to enable agents to work for a common task (i.e., composition). In particular, we propose a multi-agent Q-learning algorithm for service composition, which is expected to achieve better performance when compared with the single-agent Q-learning method and multi-agent SARSA (State-Action-Reward-State-Action) method. Our experimental results demonstrate the effectiveness and efficiency of our approach.