JetStream: Cluster-Scale Parallelization of Information Flow Queries

Andrew Quinn, David Devecsery, Peter M. Chen, and Jason Flinn

Abstract

Dynamic information flow tracking (DIFT) is an important tool in many domains, such as security, debugging, forensics, provenance, configuration troubleshooting, and privacy tracking. However, the usability of DIFT is currently limited by its high overhead; complex information flow queries can take up to two orders of magnitude longer to execute than the original execution of the program. This precludes interactive uses in which users iteratively refine queries to narrow down bugs, leaks of private data, or performance anomalies.

JetStream applies cluster computing to parallelize and accelerate information flow queries over past executions. It uses deterministic record and replay to time slice executions into distinct contiguous chunks of execution called epochs, and it tracks information flow for each epoch on a separate core in the cluster. It structures the aggregation of information flow data from each epoch as a streaming computation. Epochs are arranged in a sequential chain from the beginning to the end of program execution; relationships to program inputs (sources) are streamed forward along the chain, and relationships to program outputs (sinks) are streamed backward. JetStream is the first system to parallelize DIFT across a cluster. Our results show that JetStream queries scale to at least 128 cores over a wide range of applications. JetStream accelerates DIFT queries to run 12-48 times faster than sequential queries; in most cases, queries run faster than the original execution of the program.