Data Centers Job Scheduling with Deep Reinforcement Learning

TL;DR

This paper introduces A2cScheduler, a deep reinforcement learning approach using Advantage Actor-Critic for efficient job scheduling in data centers, demonstrating competitive performance on simulated and real workloads.

Contribution

The paper presents a novel A2C-based scheduling method that reduces gradient variance and improves efficiency in complex, heterogeneous data center environments.

Findings

Achieves competitive scheduling performance on simulated workloads.

Demonstrates effectiveness on real data from an academic data center.

Reduces gradient estimation variance compared to previous methods.

Abstract

Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.