Latest revision as of 01:57, 2 February 2021

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 line-rate 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.

Comment: SOSR 2017, Santa Clara, CA

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1611.04825

• http://dl.acm.org/ft_gateway.cfm?id=3063772&type=pdf

• http://dx.doi.org/10.1145/3050220.3063772

• https://dl.acm.org/doi/pdf/10.1145/3050220.3063772,

http://dx.doi.org/10.1145/3050220.3063772 under the license http://www.acm.org/publications/policies/copyright_policy#Background

• https://dl.acm.org/doi/pdf/10.1145/3050220.3063772,

https://ui.adsabs.harvard.edu/abs/2016arXiv161104825S/abstract,

https://dl.acm.org/citation.cfm?id=3063772,

https://dblp.uni-trier.de/db/conf/sosr/sosr2017.html#SivaramanNRMR17,

https://collaborate.princeton.edu/en/publications/heavy-hitter-detection-entirely-in-the-data-plane,

https://nyuscholars.nyu.edu/en/publications/heavy-hitter-detection-entirely-in-the-data-plane,

https://academic.microsoft.com/#/detail/2605823630

DOIS: 10.1145/3050220.3050239 10.1145/3050220.3063772