Achieving self-configuration capability in autonomic systems using case-based reasoning with a new similarity measure

A lot of activities inside human body are carried out intelligently without the explicit intervention of human itself, e.g. various actions of nervous systems, blood circulation system etc. Inspired from these natural systems, autonomic computing is an emerging concept which promises to enable such kind of self-management capabilities inside software systems. Case-based reasoning (CBR) is a methodology to solve current problems using the solutions of past problems of the similar nature. In this paper, we propose to use CBR to achieve self-configuration in autonomic systems. We introduce a new similarity measure to find nearest neighbors. We have also suggested the case preparation, case retrieval and case reuse and refinement methods to enable self-configuration in autonomic systems. To support our proposed methodology, we illustrate a case-study of Autonomic Forest Fire Application.