Online model-based adaptation for optimizing performance and dependability

Abstract

Constructing adaptive software that is capable of changing behavior at runtime is a challenging software engineering problem. However, the problem of determining when and how such a system should adapt, i.e., the system's adaptation policy, can be even more challenging. To optimize the behavior of a system over its lifetime, the policy must often take into account not only the current system state, but also the anticipated future behavior of the system. This paper presents a systematic approach based on using Markov Decision Processes to model the system and to generate optimal adaptation policies for it. In our approach, we update the model on-line based on system measurements and generate updated adaptation policies at runtime when necessary. We present the general approach and then outline its application to a distributed message dissemination system based on AT&T's iMobile platform.