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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.|
|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|
|Pages:||164 - 176|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||SOSR 2017 - Proceedings of the 2017 Symposium on SDN Research|
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