DigOver: Towards Distributed & Collaborative Overlay Fault Diagnosis Based On User-level Belief Revision

Overlay networks have emerged as a powerful and flexible platform for developing new disruptive network applications. The attractive characteristics of overlay networks such as planetary-scale distributions, user-level flexibility (e.g. overlay routing) and manageability bring to overlay fault diagnosis new challenges, which include inaccessible underlying network information, incomplete and inaccurate network status observations; dynamic symptom-fault causality relationships, and multi-layer complexity. To address these challenges, we propose a distributed user-level Belief Revision based overlay fault diagnosis technique called DigOver. DigOver can passively use observed overlay symptoms as reported by overlay monitoring agents to correlate and diagnose faults, and select the least-costly appropriate probing actions whenever necessary to enhance the passive fault reasoning results. DigOver adapts to the changes in highly dynamic overlay networks by incrementally revising user beliefs based on new observed overlay symptoms. DigOver can diagnose faults without relying on underlying network fault probabilistic quantifications (e.g. prior fault probability). Simulations and experimental studies show that DigOver can efficiently (e.g. low latency) and accurately localize root causes of overlay faults/problems, even when the observed symptoms are incomplete.