CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters
Sudarsanan Rajasekaran Manya Ghobadi Aditya Akella
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.