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Heavy-hitter detection entirely in the data plane

Author(s): Sivaraman, V; Narayana, S; Rottenstreich, O; Muthukrishnan, S; Rexford, Jennifer L.

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Abstract: Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for linerate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.
Publication Date: 3-Apr-2017
Electronic Publication Date: 2017
Citation: Sivaraman, V, Narayana, S, Rottenstreich, O, Muthukrishnan, S, Rexford, J. (2017). Heavy-hitter detection entirely in the data plane. 164 - 176. doi:10.1145/3050220.3063772
DOI: doi:10.1145/3050220.3063772
Pages: 164 - 176
Type of Material: Conference Article
Journal/Proceeding Title: SOSR 2017 - Proceedings of the 2017 Symposium on SDN Research
Version: Author's manuscript

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