Algorithms and data structures to accelerate network analysis

Journal Article · 21 April 2018 · Future Generations Computer Systems

DOI: https://doi.org/10.1016/j.future.2018.04.034 · OSTI ID:1538310

Ros-Giralt, Jordi  ^[1]; Commike, Alan ^[2]; Cullen, Peter ^[2]; Lethin, Richard ^[2]

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1. Reservoir Labs, New York, NY (United States); DOE/OSTI
2. Reservoir Labs, New York, NY (United States)

As the sheer amount of computer generated data continues to grow exponentially, new bottlenecks are unveiled that require rethinking our traditional software and hardware architectures. Here in this paper we present five algorithms and data structures (long queue emulation, lockless bimodal queues, tail early dropping, LFN tables, and multiresolution priority queues) designed to optimize the process of analyzing network traffic. We integrated these optimizations on R-Scope, a high performance network appliance that runs the Bro network analyzer, and present benchmarks showcasing performance speed ups of 5X at traffic rates of 10 Gbps.

• Research Organization: Reservoir Labs, Inc., Portland, OR (United States); Reservoir Labs, New York, NY (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC)
• Grant/Contract Number: SC0004400; SC0006343; SC0017184
• OSTI ID: 1538310
• Journal Information: Future Generations Computer Systems, Journal Name: Future Generations Computer Systems Journal Issue: C Vol. 86; ISSN 0167-739X
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English