WuKong: effective diagnosis of bugs at large system scales

A key challenge in developing large scale applications (both in system size and in input size) is finding bugs that are latent at the small scales of testing, only manifesting when a program is deployed at large scales. Traditional statistical techniques fail because no error-free run is available at deployment scales for training purposes. Prior work used scaling models to detect anomalous behavior at large scales without being trained on correct behavior at that scale. However, that work cannot localize bugs automatically. In this paper, we extend that work in three ways: (i) we develop an automatic diagnosis technique, based on feature reconstruction; (ii) we design a heuristic to effectively prune the feature space; and (iii) we validate our design through one fault-injection study, finding that our system can effectively localize bugs in a majority of cases.