Coding-based schemes for fault identification in communication networks

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 two event correlation schemes for fault identification in communication networks. Both schemes are based on the coding scheme proposed by Yemini et al. (see IEEE Commum. Magazine, vol.34, no.5, p.82-90, 1996). The causality graph model is used to describe the cause-and-effect relationships between network events. For each problem, and each symptom, a unique binary code is assigned. The use of Boolean operations on symptom code makes the correlation process very fast. A simulation model is developed to verify the effectiveness and efficiency of the proposed schemes. From simulation results, we notice that the proposed schemes not only identify multiple problems at one time but also are sensitive to noise. For both schemes, their time complexities are much lower than the time complexity of the original coding scheme