Improving Efficiency of Self-Configurable Autonomic Systems Using Clustered CBR Approach
Inspired from natural self managing behavior of the human body autonomic systems promise to inject self managing behavior in software systems Such behavior enables self configuration self healing self optimization and self protection capabilities in software systems Self configuration is required in systems where efficiency is the key issue such as real time execution environments To solve self configuration problems in autonomic systems the use of various problem solving techniques has been reported in the literature including case based reasoning The case based reasoning approach exploits past experience that can be helpful in achieving autonomic capabilities The learning process improves as more experience is added in the case base in the form of cages This results in a larger case base A larger case base reduces the efficiency in terms of corn putational cost To overcome this efficiency problem this paper suggests to cluster the case base subsequent to find the solution of the reported problem This approach reduces the search complexity by confining a new case to a relevant cluster in the case base Clustering the case base is a one time process and does not need to be repeated regularly The proposed approach presented in this paper has been outlined in the form of a new clustered CBR framework The proposed framework has been evaluated on a simulation of Autonomic Forest Fire Application (AFFA) This paper presents an outline of the simulated AFFA and results on three different clustering algorithms for clustering the case base in the proposed framework The comparison of performance of the conventional CBR approach and clustered CBR approach has been presented in terms of their Accuracy Recall and Precision (ARP) and computational efficiency.
clustering self management case based reasoning autonomic computing