MLTCP: A Distributed Technique to Approximate Centralized Flow Scheduling For Machine Learning

Abstract

This paper argues that congestion control protocols in machine learning datacenters sit at a sweet spot between centralized and distributed flow scheduling solutions. We present MLTCP, a technique to augment today's congestion control algorithms to approximate an interleaved centralized flow schedule. At the heart of MLTCP lies a straight-forward principle based on a key conceptual insight: by scaling the congestion window size (or sending rate) based on the number of bytes sent at each iteration, MLTCP flows eventually converge into a schedule that reduces network contention. We demonstrate that MLTCP uses a gradient descent trend with a step taken at every training (or fine-tuning) iteration towards reducing network congestion among competing jobs.