JetStream: Cluster-Scale Parallelization of Information Flow Queries

Authors: 

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

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. Jet- Stream 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.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@inproceedings {199329,
author = {Andrew Quinn and David Devecsery and Peter M. Chen and Jason Flinn},
title = {JetStream: Cluster-Scale Parallelization of Information Flow Queries},
booktitle = {12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16)},
year = {2016},
isbn = {978-1-931971-33-1},
address = {Savannah, GA},
pages = {451--466},
url = {https://www.usenix.org/conference/osdi16/technical-sessions/presentation/quinn},
publisher = {{USENIX} Association},
month = nov,
}

Presentation Audio