Taurus: A Data Plane Architecture for Per-Packet ML. (arXiv:2002.08987v2 [cs.NI] UPDATED)

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.

In this work, we present the design and implementation of …

architecture arxiv data ml