Practical Heavy-Hitter Detection Algorithms for Programmable Switches

Programmable switches enable offloading various network functions, such as anomaly detection and traffic engineering, to the same switches that perform packet routing. A fundamental component of many such applications is detecting heavy hitters (largest flows). Realizing such data plane algorithms requires taking into consideration all types of limited hardware resources of the switch, including the recirculation bandwidth, number of stages, and memory. This motivates solutions that avoid recirculation, fit into a minimal number of stages, and are memory-frugal. We introduce a novel sketch for heavy hitter detection, CMSIS, that supports both online detection and offline retrieval of heavy hitters and achieves high accuracy while incurring low resource consumption. We implemented CMSIS in P4 for the Tofino 2 target. This implementation of CMSIS requires no recirculation and consumes 25% less pipeline stages than state-of-the-art alternatives that do not perform recirculation, while its memory consumption is competitive with prior works.