Analyzing policy dependencies using historical information

Autonomic computing is central to the success of IT infrastructure deployment as its complexity and pervasiveness grows. This paper addresses one aspect of policy-based autonomic computing - the issue of identifying dependencies between policies, knowledge of which is useful to the policymaker while defining or updating policies. These dependencies are determined via assesment of the impact of a policy on the sensors (measurable entities at runtime). Our approach uses a simple pragmatic model over the measured runtime information from the recent past. Both static and runtime information is combined to provide effective feedback.