Enabling Self-Configuration in Autonomic Systems Using Case-Based Reasoning with Improved Efficiency

Autonomic computing is an emerging philosophy which promises to enable self-management capabilities in software systems. These self-management properties include self-configuration, self-healing, self-protection, self-optimization, self-awareness and self-governance. Enabling any of these properties in software systems is an open challenge. Exhibiting such self-management behavior is a continuous process in the software life cycle. Case-based reasoning is a problem solving methodology which exploits past experience. Past experience is maintained in the form of problem-solution pairs, also called cases. On the arrival of new problem, solution of past similar problems is used after appropriate adaptation. This problem solving technique can be used to achieve some of the properties of autonomic systems based on experience. To find this solution, entire experience space is searched which reduces efficiency. To overcome this efficiency problem, we restrict the fast growth of case repository, so that every time we have to search a very limited number of cases. We applied the proposed approach on a simulation of Autonomic Forest Fire application for self-configuration capability. Our results show that the proposed approach is quite promising in terms of accuracy as well as efficiency.