The soaring use of machine learning leads to increasing processing demands. As data volume keeps growing, providing classification services with good machine learning performance, high throughput, low latency, and minimal equipment overheads becomes a challenge. Offloading machine learning tasks to network switches can be a scalable solution to this problem, providing high throughput and low latency. However, network devices are resource constrained, and lack support for machine learning functionality. In this paper, we introduce IIsy -a novel mapping tool of machine learning classification models to off-the-shelf switches. Using an efficient encoding algorithm, enables fitting a range of classification models on switches, co-existing with standard switch functionality. To overcome resource constraints, adopts a hybrid approach for ensemble models, running a small model on a switch and a large model on the backend. The evaluation shows that achieves near-optimal classification results, within minimum resource overheads, and while reducing the load on the backend by 70% for data-intensive use cases.

Machine learning is currently driving a technological and societal revolution. While programmable switches have been proven to be useful for in-network computing, machine learning within programmable switches had little success so far. Not using network devices for machine learning has a high toll, given the known power efficiency and performance benefits of processing within the network. In this paper, we explore the potential use of commodity programmable switches for in-network classification, by mapping trained machine learning models to match-action pipelines. We introduce IIsy, a software and hardware based prototype of our approach, and discuss the suitability of mapping to different targets. Our solution can be generalized to additional machine learning algorithms, using the methods presented in this work.