Per Flow Packet Sampling for High-Speed Network Monitoring

Abstract

We present a per-flow packet sampling method that enables the real-time classification of high-speed network traffic. Our method, based upon the partial sampling of each flow (i.e., performing sampling at only early stages in each flow’s lifetime), provides a sufficient reduction in total traffic (e.g., a factor of five in packets, a factor of ten in bytes) as to allow practical implementations at one Gigabit/s, and, using limited hardware assistance, ten Gigabit/s.

Publication
Proceedings of the First International Conference on Communication Systems and Networks (COMSNETS’09)
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Marco Canini
Associate Professor of Computer Science

My current interest is in designing better systems support for AI/ML and provide practical implementations deployable in the real-world.