Reasoning under Uncertainty for Overlay Fault Diagnosis

The performance and reliability of overlay services rely on the underlying overlay network´s ability to effectively diagnose and recover from faults such as link failures and overlay node outages. However, overlay networks bring to fault diagnosis new challenges such as large-scale deployment, inaccessible underlay network information, dynamic symptom-fault causality relationship, and multi-layer complexity. In this paper, we develop an evidential overlay fault diagnosis framework called DigOver to tackle these challenges. Firstly, DigOver identifies a set of potential faulty components based on shared end-user observed negative symptoms. Then, each potential faulty component is evaluated to quantify its fault likelihood and the corresponding evaluation uncertainty. Finally, DigOver dynamically constructs a plausible fault graph to locate the root causes of end-user observed negative symptoms. Both simulation and Internet experiments demonstrate that DigOver can effectively and accurately diagnose overlay faults based on end-user observed negative symptoms.