Network Contention-Aware Cluster Scheduling with Reinforcement Learning
Junyeol Ryu Jeongyoon Eo
With continuous advances in deep learning, distributed training is becoming
common in GPU clusters. Specifically, for emerging workloads with diverse
amounts, ratios, and patterns of communication, we observe that network
contention can significantly degrade training throughput. However, widely used
scheduling policies often face limitations as they are agnostic to network
contention between jobs. In this paper, we present a new approach to mitigate
network contention in GPU clusters using reinforcement learning. We formulate
GPU cluster scheduling as a reinforcement learning problem and opt to learn a
network contention-aware scheduling policy that efficiently captures contention
sensitivities and dynamically adapts scheduling decisions through continuous
evaluation and improvement. We show that compared to widely used scheduling
policies, our approach reduces average job completion time by up to 18.2\% and
effectively cuts the tail job completion time by up to 20.7\% while allowing a
preferable trade-off between average job completion time and resource
utilization.