Chic: experience-driven scheduling in machine learning clusters

Yifan Gong, Baochun Li, Ben Liang, Zheng Zhan

Abstract: Large-scale machine learning (ML) models are routinely trained in a distributed fashion, due to their increasing complexity and data sizes. In a shared cluster handling multiple distributed learning workloads with a parameter server framework, it is important to determine the adequate number of concurrent workers and parameter servers for each ML workload over time, in order to minimize the average completion time and increase resource utilization. Existing schedulers for machine learning workloads involve meticulously designed heuristics. However, as the execution environment is highly complex and dynamic, it is challenging to construct an accurate model to make online decisions. In this paper, we design an experience-driven approach that learns to manage the cluster directly from experience rather than using a mathematical model. We propose Chic, a scheduler that is tailored for scheduling machine learning workloads in a cluster by leveraging deep reinforcement learning techniques. With our design of the state space, action space, and reward function, Chic trains a deep neural network with a modified version of the cross-entropy method to approximate the policy for assigning workers and parameter servers for future workloads based on the experience of the agent. Furthermore, a simplified version named Chic-Pair with a shorter training time for the policy is purposed by assigning workers and parameter servers in a pair. We compare Chic and Pair with state-of-the-art heuristics, and our results show that Chic and Chic-Pair are able to reduce the average training time significantly for machine learning workloads under a wide variety of conditions.