SwiSh: Distributed Shared State Abstractions for Programmable Switches


Lior Zeno, Technion; Dan R. K. Ports, Jacob Nelson, and Daehyeok Kim, Microsoft Research; Shir Landau Feibish, The Open University of Israel; Idit Keidar, Arik Rinberg, Alon Rashelbach, Igor De-Paula, and Mark Silberstein, Technion


We design and evaluate SwiSh, a distributed shared state management layer for data-plane P4 programs. SwiSh enables running scalable stateful distributed network functions on programmable switches entirely in the data-plane. We explore several schemes to build a shared variable abstraction, which differ in consistency, performance, and in-switch implementation complexity. We introduce the novel Strong Delayed-Writes (SDW) protocol which offers consistent snapshots of shared data-plane objects with semantics known as r-relaxed strong linearizability, enabling implementation of distributed concurrent sketches with precise error bounds.

We implement strong, eventual, and SDW consistency protocols in Tofino switches, and compare their performance in microbenchmarks and three realistic network functions, NAT, DDoS detector, and rate limiter. Our results show that the distributed state management in the data plane is practical, and outperforms centralized solutions by up to four orders of magnitude in update throughput and replication latency.

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

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@inproceedings {278302,
author = {Lior Zeno and Dan R. K. Ports and Jacob Nelson and Daehyeok Kim and Shir Landau-Feibish and Idit Keidar and Arik Rinberg and Alon Rashelbach and Igor De-Paula and Mark Silberstein},
title = {{SwiSh}: Distributed Shared State Abstractions for Programmable Switches},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {171--191},
url = {https://www.usenix.org/conference/nsdi22/presentation/zeno},
publisher = {USENIX Association},
month = apr,

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