User-plane machine learning enables low-latency and high-throughput inference at line rate. Yet, data planes are highly constrained environments, and restrictions are especially marked in programmable switches with limited available memory and minimum support for mathematical operations or data types. As a result, current solutions for in-switch inference that are compatible with production-level hardware lack support for complex features or suffer from limited scalability, which creates performance barriers in complex tasks involving large decision spaces. We address this limitation and present Flowrest, a first complete Random Forest (RF) model implementation that can operate at the level of individual flows in commercial switches. Our solution builds on (i) a novel framework to embed generic flow-level machine learning models into programmable switch ASICs, and (ii) original guidelines for tailoring RF models to operations in programmable switches already at the design stage. We implement Flowrest as open-source software using the P4 language and assess its performance in an experimental platform based on Intel Tofino switches. Tests with tasks of unprecedented complexity show how our model can improve accuracy by up to 39% over previous approaches to implement RF models in real-world equipment. Flowrest has been accepted to appear in the proceedings of INFOCOM 2023.
About Aristide Tanyi-Jong Akem
Akem is a PhD student in the Networks Data Science Group at IMDEA Networks Institute in Madrid, Spain. He is also a student at Universidad Carlos III de Madrid, where he is enrolled in the Telematics Engineering program. Prior to his PhD studies, he completed an engineering degree at the University of Yaounde I, in Cameroon and a master’s in electrical and computer engineering at Carnegie Mellon University Africa, in Rwanda. There, his research was on machine learning for energy-saving optimization in cellular networks. He is currently involved with the European Union’s Horizon 2020 project “BANYAN” which aims to bring big data analytics to radio access networks. At the moment, he is visiting Orange Labs in Paris, France as part of the secondments of the project. Akem’s current research interest is in the area of in-band network intelligence, with a focus on in-network machine learning
This event will be conducted in English