אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב
Alon Rashelbach, Lior Zeno, Haggai Eran
יום רביעי, 30.03.2022, 11:30
Room 861, EE Meyer Building and zoom Lecture: 96624383219
In this CE club session, we will present three papers that will appear next week at NSDI'22:
Scaling Open vSwitch with a Computational Cache
by Alon Rashelbach
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 x and 12.3 x for the first and second designs, respectively, with the latter also supporting up to 60K OpenFlow rule updates/second, by far exceeding the original OVS.
*Joint work with Ori Rottenstreich and Mark Silberstein
SwiSh: Distributed Shared State Abstractions for Programmable Switches
by Lior Zeno
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 strong r-relaxed 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 demonstrate that the general distributed state management in the data plane is practical, and outperforms any centralized solution by up to four orders of magnitude in update throughput and replication latency.
*Joint work with Dan Ports, Jacob Nelson, Daehyeok Kim, Shir Landau Feibish, Idit Keidar, Arik Rinberg, Alon Rashelbach, Igor De-Paula and Mark Silberstein
An Edge-queued Datagram Service for all Data-center Traffic
by Haggai Eran
Modern datacenters support a wide range of protocols and in-network switch enhancements aimed at improving performance. Unfortunately, most new and legacy protocols and enhancements often don’t coexist gracefully because they inevitably interact via queuing in the network. In this paper we describe EQDS, a new datagram service for datacenters that moves almost all of the queuing out of the core network and into the sending host. This enables it to support multiple (conflicting) higher layer protocols, while only sending packets into the network when decided by a receiver-driven credit scheme. EQDS can speed-up legacy TCP and RDMA stacks and enable transport protocol evolution, while benefiting from future switch enhancements without needing to modify higher layer stacks. We show through simulation and multiple implementations that EQDS can reduce FCT of legacy TCP by 2x, improve the NVMeOF throughput by 30%, and safely run TCP alongside RDMA on the same network.
*Joint work with Costin Raiciu, Vladimir Olteanu, Adrian Popa, Mark Handley, Mark Silberstein, Dragos Dumitrescu, Cristi Baciu, Georgios Nikolaidis