Model-based fault localization in largescale computing systems

We propose a new fault localization technique for software bugs in large-scale computing systems. Our technique always collects per-process function call traces of a target system, and derives a concise execu-tion model that reflects its normal function calling be-haviors using the traces. To find the cause of a failure, we compare the derived model with the traces collected when the system failed, and compute a suspect score that quantifies how likely a particular part of call traces ex-plains the failure. The execution model consists of a call probability of each function in the system that we esti-mate using the normal traces. Functions with low prob-abilities in the model give high anomaly scores when called upon a failure. Frequently-called functions in the model also give high scores when not called. Finally, we report the function call sequences ranked with the suspect scores to the human analyst, narrowing further manual localization down to a small part of the overall system. We have applied our proposed method to fault localization of a known non-deterministic bug in a dis-tributed parallel job manager. Experimental results on a three-site, 78-node distributed environment demonstrate that our method quickly locates an anomalous event that is highly correlated with the bug, indicating the effec-tiveness of our approach.