Michael Tighe

Date of Award


Degree Type


Degree Name

Master of Science


Computer Science


Dr. Michael Bauer


Policy-based Autonomic Management monitors a system and its applications and tweaks performance parameters in real-time based on a set of governing policies. A policy specifies a set of conditions under which one or more of a set of actions are to be performed. It is very common that multiple policies’ conditions are met simultaneously, each advocating many actions. Deciding which action to perform is a non-trivial task. We propose a method of diagnosing the system to try to determine the best action or actions to perform in a given situation using Abductive Inference. We develop an original method of building a causality graph to facilitate diagnosis directly from a set of policies. Performance of the diagnosis method is measured by implementing diagnosis into an existing autonomic management application and monitoring the performance of a LAMP (Linux, Apache, MySQL, PHP) server being governed by the manager. The results are favourable when compared to previous methods of action selection and to the server running without the autonomic manager.



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