Towards Collaborative User-Level Overlay Fault Diagnosis

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 collaborative overlay User Observation based fault diagnosis technique called OUD. OUD can passively use observed overlay symptoms as reported by overlay monitoring agents to correlate multiple users' observations to diagnose faults. OUD can diagnose faults without relying on underlying network fault probabilistic quantifications (e.g. prior fault probability). Simulations and experimental studies show that OUD can efficiently (e.g. low latency) and accurately localize root causes of overlay faults/problems, even when the observed symptoms are incomplete.