Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling

TL;DR

This paper applies deep reinforcement learning to optimize multi-resource, multi-machine job scheduling in data centers, demonstrating potential improvements over traditional algorithms in complex environments.

Contribution

It extends a recent deep reinforcement learning-based scheduling algorithm to multiple server clusters, showing enhanced performance in complex data center scenarios.

Findings

Deep reinforcement learning can outperform traditional scheduling algorithms.

The extended method effectively manages multi-resource, multi-machine environments.

Potential for improved efficiency in data center job scheduling.

Abstract

Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. Our study reveals that deep reinforcement learning method has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments.