Methodology for Automatic Inference of Task Hierarchies in Complex Systems

Abstract

Distributed systems are hard to debug and analysis.Understanding system runtime behavior is key to system debugging and analysis.Existing works describe system runtime behavior as causal paths,but these works requires either manual annotation or developer-provided execution structures.This paper describes methodology to automatically infer hierarchical task models for complex systems.By using instrumentation,it can automatically infer task models,including task boundaries and causal relations among tasks,based a set of general heuristics.By using clustering,it further infer hierarchical structures on generated task models.By applying the inference methodology on real systems(Apache and PacificA),it concludes that the hierarchical task models help both understanding system runtime behavior and debugging performance bugs.