A Randomized Partitioning Approach for CBR-Based Autonomic Systems to Improve Retrieval Performance

Malik Jahan Khan, Mian Muhammad Awais, Shafay Shamail

Abstract: Autonomic systems exhibit self-managing behavior using various algorithms. Case-based reasoning is one the techniques that enable the autonomic manager to learn from past experience. Case-base is partitioned into some clusters in order to improve the retrieval efficiency. Deciding an appropriate number of clusters for a case-base is not a trivial problem. This paper proposes a randomized algorithm for determining the number of clusters to be formed of the case-base. Subsequently, a binary search-based case retrieval strategy has been applied to ensure enhanced retrieval time performance. The paper presents two versions of the randomized algorithm. The first version guarantees success but its computational cost is a function of random variable; the other guarantees a deterministic computational cost but success is not guaranteed. The performance of the proposed algorithms has been reported on a simulated case study of the Autonomic Forest Fire Application.