Efficient Measurement on Programmable Switches Using Probabilistic Recirculation

TL;DR

This paper introduces PRECISION, a probabilistic recirculation algorithm for programmable switches that improves heavy hitter detection accuracy by overcoming architectural constraints.

Contribution

It presents a novel measurement algorithm, PRECISION, utilizing probabilistic recirculation to enhance accuracy within programmable switch limitations.

Findings

PRECISION outperforms previous algorithms in accuracy.

Probabilistic recirculation enables better stateful memory access.

Architectural constraints impact measurement accuracy, guiding future design.

Abstract

Programmable network switches promise flexibility and high throughput, enabling applications such as load balancing and traffic engineering. Network measurement is a fundamental building block for such applications, including tasks such as the identification of heavy hitters (largest flows) or the detection of traffic changes. However, high-throughput packet processing architectures place certain limitations on the programming model, such as restricted branching, limited capability for memory access, and a limited number of processing stages. These limitations restrict the types of measurement algorithms that can run on programmable switches. In this paper, we focus on the RMT programmable high-throughput switch architecture, and carefully examine its constraints on designing measurement algorithms. We demonstrate our findings while solving the heavy hitter problem. We introduce PRECISION, an algorithm that uses \emph{Probabilistic Recirculation} to find top flows on a programmable switch. By recirculating a small fraction of packets, PRECISION simplifies the access to stateful memory to conform with RMT limitations and achieves higher accuracy than previous heavy hitter detection algorithms that avoid recirculation. We also analyze the effect of each architectural constraint on the measurement accuracy and provide insights for measurement algorithm designers.