SketchLib: Enabling Efficient Sketch-based Monitoring on Programmable Switches

Authors: 

Hun Namkung, Carnegie Mellon University; Zaoxing Liu, Boston University; Daehyeok Kim, Carnegie Mellon University and Microsoft; Vyas Sekar and Peter Steenkiste, Carnegie Mellon University

Abstract: 

Sketching algorithms or sketches enable accurate network measurement results with low resource footprints. While emerging programmable switches are an attractive target to get these benefits, current implementations of sketches are either inefficient and/or infeasible on hardware. Our contributions in the paper are: (1) systematically analyzing the resource bottlenecks of existing sketch implementations in hardware; (2) identifying practical and correct-by-construction optimization techniques to tackle the identified bottlenecks; and (3) designing an easy-to-use library called SketchLib to help developers efficiently implement their sketch algorithms in switch hardware to benefit from these resource optimizations. Our evaluation on state-of-the-art sketches demonstrates that SketchLib reduces the hardware resource footprint up to 96% without impacting fidelity.

NSDI '22 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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