Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling
This work addresses job scheduling inefficiencies in data centers, but it is incremental as it builds on an existing algorithm.
The paper tackled the problem of minimizing job scheduling time in data center networks by improving a recent algorithm using deep reinforcement learning and extending it to multiple server clusters, resulting in the method outperforming traditional resource allocation algorithms in complex environments.
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.