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Fast detection of heavy flows (e.g., heavy hitters and heavy changers) inmassive network traffic is challenging due to the stringent requirements offast packet processing and limited resource availability. Invertible sketchesare summary data structures that can recover heavy flows with small memoryfootprints and bounded errors, yet existing invertible sketches incur highmemory access overhead that leads to performance degradation. We presentMV-Sketch, a fast and compact invertible sketch that supports heavy flowdetection with small and static memory allocation. MV-Sketch tracks candidateheavy flows inside the sketch data structure via the idea of majority voting,such that it incurs small memory access overhead in both update and queryoperations, while achieving high detection accuracy. We present theoreticalanalysis on the memory usage, performance, and accuracy of MV-Sketch in bothlocal and network-wide scenarios. We further show how MV-Sketch can beimplemented and deployed on P4-based programmable switches subject to hardwaredeployment constraints. We conduct evaluation in both software and hardwareenvironments. Trace-driven evaluation in software shows that MV-Sketch achieveshigher accuracy than existing invertible sketches, with up to 3.38x throughputgain. We also show how to boost the performance of MV-Sketch with SIMDinstructions. Furthermore, we evaluate MV-Sketch on a Barefoot Tofino switchand show how MV-Sketch achieves line-rate measurement with limited hardwareresource overhead.

A Fast and Compact Invertible Sketch for Network-Wide Heavy Flow Detection