Exploiting Fuzzy Clustering and Case-Based Reasoning for Autonomic Managers

Cynthia Khan, Malik Jahan Khan

Abstract: Designing efficient self-management algorithms for autonomic managers has been an ongoing and evolving research area. In literature, many machine learning paradigms have been proposed and exploited to make effective decisions in autonomic managers. This position paper proposes to optimize the decision making algorithm closer to the nature inspired decision making process. Core of the proposed framework is case-based reasoning which is an incremental learning mechanism for solving new system problems using the past experience. Fuzzy clustering has been proposed to maintain the knowledge-base of past problems in autonomic managers. New monitored problem in autonomic manager seeks fuzzy memberships amongst different clusters in the knowledge-base and problem solver in autonomic manager exploits existing solutions in different clusters with respect to their fuzzy membership value. It has been tested on a simulated case-study of Autonomic Forest Fire Application and up to 88% accurate predictions have been observed.