Sketchovsky: Enabling Ensembles of Sketches on Programmable Switches


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


Network operators need to run diverse measurement tasks on programmable switches to support management decisions (e.g., traffic engineering or anomaly detection). While prior work has shown the viability of running a single sketch instance, they largely ignore the problem of running an ensemble of sketch instances for a collection of measurement tasks. As such, existing efforts fall short of efficiently supporting a general ensemble of sketch instances. In this work, we present the design and implementation of Sketchovsky, a novel cross-sketch optimization and composition framework. We identify five new cross-sketch optimization building blocks to reduce critical switch hardware resources. We design efficient heuristics to select and apply these building blocks for arbitrary ensembles. To simplify developer effort, Sketchovsky automatically generates the composed code to be input to the hardware compiler. Our evaluation shows that Sketchovsky makes ensembles with up to 18 sketch instances become feasible and can reduce up to 45% of the critical hardware resources.

NSDI '23 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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@inproceedings {286431,
author = {Hun Namkung and Zaoxing Liu and Daehyeok Kim and Vyas Sekar and Peter Steenkiste},
title = {Sketchovsky: Enabling Ensembles of Sketches on Programmable Switches},
booktitle = {20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
year = {2023},
isbn = {978-1-939133-33-5},
address = {Boston, MA},
pages = {1273--1292},
url = {},
publisher = {USENIX Association},
month = apr

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