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

arXiv: https://arxiv.org/abs/1711.07440 · HTML: https://mlsanity.com/paper/1711.07440/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [Towards Scalable Verification of Deep Reinforcement Learning](https://mlsanity.com/paper/2105.11931/) (2021)
- [Verifying Generalization in Deep Learning](https://mlsanity.com/paper/2302.05745/) (2023)
- [NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming](https://mlsanity.com/paper/2109.12171/) (2021)
- [Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment](https://mlsanity.com/paper/2203.03021/) (2022)
- [RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources](https://mlsanity.com/paper/2205.08350/) (2022)
- [Reinforcement Learning for Dynamic Memory Allocation](https://mlsanity.com/paper/2410.15492/) (2024)

## Abstract (arXiv)

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.