An extensible framework for repair-driven monitoring

In recent years autonomic computing, specifically autonomic data centre management has gained significant attention. Human intervention be minimized to reduce the operating costs of business applications. In this paper we focus our attention to the self-repair dimension and present a flexible probabilistic framework to develop agents for self-repair in the context of business-information-system components. Our framework seeks to pick the optimal sequence of repair actions given only imperfect information about the experienced fault. In contrast to existing recovery-oriented approaches, our model explicitly considers fault prevalence, symptoms of recurrent failures, and inclusive repair actions. We evaluate our proposal using discrete event simulation. Our evaluation shows that an optimal repair policy can be computed from a brief specification of repair actions. Even in the context of very unreliable error detection our controller is able to estimate the current state of the monitored system and recover from failure.