State-Compute Replication: Parallelizing High-Speed Stateful Packet Processing
Qiongwen Xu, Sebastiano Miano, Xiangyu Gao, Tao Wang, Adithya, Murugadass, Songyuan Zhang, Anirudh Sivaraman, Gianni Antichi, Srinivas, Narayana

TL;DR
This paper proposes state-compute replication, a novel method to scale high-speed stateful packet processing across multiple CPU cores by using replication and a packet history sequencer, overcoming limitations of traditional sharding methods.
Contribution
It introduces state-compute replication, enabling linear scalability of throughput for stateful packet processing across cores without synchronization, independent of flow size distributions.
Findings
Achieves linear throughput scaling with cores
Works effectively with realistic data center and Internet traces
Eliminates flow size limitations of sharding methods
Abstract
With the slowdown of Moore's law, CPU-oriented packet processing in software will be significantly outpaced by emerging line speeds of network interface cards (NICs). Single-core packet-processing throughput has saturated. We consider the problem of high-speed packet processing with multiple CPU cores. The key challenge is state--memory that multiple packets must read and update. The prevailing method to scale throughput with multiple cores involves state sharding, processing all packets that update the same state, i.e., flow, at the same core. However, given the heavy-tailed nature of realistic flow size distributions, this method will be untenable in the near future, since total throughput is severely limited by single core performance. This paper introduces state-compute replication, a principle to scale the throughput of a single stateful flow across multiple cores using…
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Taxonomy
TopicsInterconnection Networks and Systems · Caching and Content Delivery · Software-Defined Networks and 5G
