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.

Emerging applications---cloud computing, the internet of things, and augmented/virtual reality---demand responsive, secure, and scalable datacenter networks. These networks currently implement simple, per-packet, data-plane heuristics (e.g., ECMP and sketches) under a slow, millisecond-latency control plane that runs data-driven performance and security policies. However, to meet applications' service-level objectives (SLOs) in a modern data center, networks must bridge the gap between line-rate, per-packet execution and complex decision making.

Meeting Service-Level Objectives (SLOs) for workloads in today's datacenter environments places stringent demands on end-host servers: work conservation, tolerance to varying request service time distributions, high throughput, and CPU efficiency. Beginning with Receive Side Scaling (RSS), various schedulers have been proposed to steer packets to cores while preserving locality. However, these techniques are either too inflexible (randomly steering traffic at the NIC) or slow (bottlenecked by a central CPU-based scheduler).