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.

CASSINI是一个面向机器学习聚类的网络感知作业调度器，引入了一种新颖的几何抽象来考虑不同作业的通信模式，在网络链接上放置它们。通过使用亲和图，CASSINI找到一系列时间偏移值来调整一组作业的通信阶段，以便共享相同网络链接的作业的通信模式交错在一起。在一个24台服务器的测试环境中对13个常见机器学习模型进行的实验证明，与最先进的机器学习调度器相比，CASSINI将作业的平均完成时间和尾部完成时间分别提高了1.6倍和2.5倍。此外，我们展示了CASSINI将集群中标记的ECN数据包数量减少了多达33倍。

CASSINI: 机器学习集群中的网络感知作业调度