Homunculus: Auto-Generating Efficient Data-Plane ML Pipelines for Datacenter Networks

Abstract

Support for Machine Learning (ML) applications in networking has significantly improved over the last decade. The availability of public datasets and programmable switching fabrics (including low-level languages to program them) presents a full-stack to the programmer for deploying in-network ML. However, the diversity of tools involved, coupled with complex optimization tasks of ML model design and hyperparameter tuning while complying with the network constraints (like throughput and latency), puts the onus on the network operator to be an expert in ML, network design, and programmable hardware. We present Homunculus, a high-level framework that enables network operators to specify their ML requirements in a declarative rather than imperative way. Homunculus takes as input the training data and accompanying network and hardware constraints, and automatically generates and installs a suitable model onto the underlying switching target. It performs model design-space exploration, training, and platform code-generation as compiler stages, leaving network operators to focus on acquiring high-quality network data. Our evaluations on real-world ML applications show that Homunculus's generated models achieve up to 12% better F1 scores compared to hand-tuned alternatives, while operating within the resource limits of the underlying targets. We further demonstrate the high performance and increased reactivity (seconds to nanoseconds) of the generated models on emerging per-packet ML platforms to showcase Homunculus's timely and practical significance.