An Autonomic Problem Determination and Remediation Agent for Ambiguous Situations Based on Singular Value Decomposition Technique

The growing cost of managing computer systems leads to the development of autonomic systems. Problem determination and remediation (PDR) play an important role in an autonomic computer system, especially in ambiguous and unexpected error situations due to operating environment and requirement changes. A PDR agent system that can learn and adapt to changing environments, react to existing or new error situations and predict possible problems and take actions proactively is an integral part of an autonomic system. Traditional static problem determination and remediation system becomes insufficient. In this paper, we introduce a mathematical technique called "singular value decomposition (SVD)" with a feedback system to enable a PDR agent to be responsive to ambiguous and undefined situations, predict possible problems and take actions proactively, and be able to adapt itself to the changing environment. This approach treats the situations-remediation actions relationship as a statistical problem. Using SVD technique, implicit higher order structure in situations and remediation actions association is modeled and extracted, coupled with a feedback system with human interactions, the agent is able to learn from new error situations and derive the corresponding remediation actions.