Efficient Active Probing for Fault Diagnosis in Large Scale and Noisy Networks

Active probing is an effective tool for monitoring networks. By measuring probing responses, we can perform fault diagnosis actively and efficiently without instrumentation on managed entities. In order to reduce the traffic generated by probing messages and the measurement infrastructure costs, an optimal set of probes is desirable. However, the computational complexity for obtaining such an optimal set is very high. Existing works assume single-fault scenarios, apply only to small size networks, or use simplistic methods that are vulnerable to noises. In this paper, by exploiting the conditionally independent property in Bayesian networks, we prove a theorem on the information provided by a set of probes. Based on this theorem and structure property of Bayesian networks, we propose two approaches which can effectively reduce the computation time. A highly efficient adaptive probing algorithm is then presented. Compared with previous techniques, experiments have shown that our approach is more efficient in selecting an optimal set of probes without degrading diagnosis quality in large scale and noisy networks.