Robust event correlation scheme for fault identification in communication networks

Abstract

The complexity of communication networks and the amount of information transferred in these networks have made the management of such networks increasingly difficult. Since faults are inevitable, quick detection, identification, and recovery are crucial to make the systems more robust and their operation more reliable. This paper proposes a novel event correlation scheme for fault identification in communication networks. This scheme is based on the algebraic operations of sets. The causality graph model is used to describe the cause-and-effect relationships between network events. For each disorder, and each manifestation, a unique prime number is assigned. The use of the greatest common devisor (GCD) makes the correlation process simple and fast. A simulation model is developed to verify the effectiveness and efficiency of the proposed scheme. From simulation results, we notice that this scheme not only identifies multiple disorders at one time but also is insensitive to noise. The time complexity of the correlation process is close to a function of n, where n is the number of observed manifestations, with order O(n(2)); therefore, the on-line fault identification is easy to achieve. Copyright (C) 1999 John Wiley & Sons, Ltd.