CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters

TL;DR

CASSINI is a network-aware job scheduler for ML clusters that optimizes communication patterns, significantly improving job completion times and reducing network congestion compared to existing schedulers.

Contribution

It introduces a geometric abstraction and affinity graph-based approach to interleave communication phases, enhancing network utilization in ML job scheduling.

Findings

Up to 1.6x faster average job completion time

Up to 2.5x faster tail job completion time

Reduces ECN marked packets by up to 33x

Abstract

We present CASSINI, a network-aware job scheduler for machine learning (ML) clusters. CASSINI introduces a novel geometric abstraction to consider the communication pattern of different jobs while placing them on network links. To do so, CASSINI uses an affinity graph that finds a series of time-shift values to adjust the communication phases of a subset of jobs, such that the communication patterns of jobs sharing the same network link are interleaved with each other. Experiments with 13 common ML models on a 24-server testbed demonstrate that compared to the state-of-the-art ML schedulers, CASSINI improves the average and tail completion time of jobs by up to 1.6x and 2.5x, respectively. Moreover, we show that CASSINI reduces the number of ECN marked packets in the cluster by up to 33x.