Sharing end-user negative symptoms for improving overlay network dependability

Abstract

The dependability of overlay services rely on the overlay network's capabilities to effectively diagnose and recover faults (e.g., link failures, overlay node outages). However, overlay applications bring to overlay fault diagnosis new challenges, which include large-scale deployment, inaccessible underlying 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, the 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, the DigOver dynamically constructs a plausible fault graph to locate the root causes of end-user observed negative symptoms.