CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters

Understand

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.