Distributed time-aware provenance

This work proves that the DTaP model provides a sound and complete representation that correctly captures dependencies among events in a distributed system and demonstrates that DistTape can efficiently maintain and query time-aware provenance at low communication and computation cost.

Abstract

The ability to reason about changes in a distributed system's state enables network administrators to better diagnose protocol misconfigurations, detect intrusions, and pinpoint performance bottlenecks. We propose a novel provenance model called Distributed Time-aware Provenance (DTaP) that aids forensics and debugging in distributed systems by explicitly representing time, distributed state, and state changes. Using a distributed Datalog abstraction for modeling distributed protocols, we prove that the DTaP model provides a sound and complete representation that correctly captures dependencies among events in a distributed system. We additionally introduce DistTape, an implementation of the DTaP model that uses novel distributed storage structures, query processing, and cost-based optimization techniques to efficiently query time-aware provenance in a distributed setting. Using two example systems (declarative network routing and Hadoop MapReduce), we demonstrate that DistTape can efficiently maintain and query time-aware provenance at low communication and computation cost.