Adaptive service composition based on reinforcement learning

Abstract

The services on the Internet are evolving. The various properties of the services, such as their prices and performance, keep changing. To ensure user satisfaction in the long run, it is desirable that a service composition can automatically adapt to these changes. To this end, we propose a mechanism for adaptive service composition. The mechanism requires no prior knowledge about services' quality, while being able to achieve the optimal composition solution by leveraging the technology of reinforcement learning. In addition, it allows a composite service to dynamically adjust itself to fit a varying environment, where the properties of the component services continue changing. We present the design of our mechanism, and demonstrate its effectiveness through an extensive experimental evaluation. © 2010 Springer-Verlag.