Scaling Open vSwitch with a Computational Cache


Alon Rashelbach, Ori Rottenstreich, and Mark Silberstein, Technion


Open vSwitch (OVS) is a widely used open-source virtual switch implementation. In this work, we seek to scale up OVS to support hundreds of thousands of OpenFlow rules by accelerating the core component of its data-path — the packet classification mechanism. To do so we use NuevoMatch, a recent algorithm that uses neural network inference to match packets, and promises significant scalability and performance benefits. We overcome the primary algorithmic challenge of the slow rule update rate in the vanilla NuevoMatch, speeding it up by over three orders of magnitude. This improvement enables two design options to integrate NuevoMatch with OVS: (1) using it as an extra caching layer in front of OVS's megaflow cache, and (2) using it to completely replace OVS's data-path while performing classification directly on OpenFlow rules, and obviating control-path upcalls. Our comprehensive evaluation on real-world packet traces and between 1K to 500K ClassBench rules demonstrates the geometric mean speedups of 1.9× and 12.3× for the first and second designs, respectively, for 500K rules, with the latter also supporting up to 60K OpenFlow rule updates/second, by far exceeding the original OVS.

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

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This content is available to:

@inproceedings {278356,
author = {Alon Rashelbach and Ori Rottenstreich and Mark Silberstein},
title = {Scaling Open {vSwitch} with a Computational Cache},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {1359--1374},
url = {},
publisher = {USENIX Association},
month = apr,

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