Failure prediction based on multi-scale frequent anomalous behavior identification in support of autonomic networks

In this paper, we present a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of individual network parameters. The proposed Frequent Anomalous Behavior Mining (FABM) algorithm utilizes multiple levels of time-scale analysis to reveal the frequent anomalous behaviors. This makes the proposed algorithm robust to unreliable, redundant, incomplete and contradictory information. FABM is simple, has low order polynomial computational complexity of O(n²), the patterns identified by FABM require space complexity of O(n) to be stored in the knowledge base of the prediction engine, provides quick and accurate response and can be easily adapted to a distributed environment. Moreover, the empirical results gathered show that using FABM an efficient prediction engine can be realized with high true positive and true negative rates.