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About RL Schedule
This document proposes using deep reinforcement learning (RL) to develop an autonomous job scheduler, called RLScheduler, for high-performance computing (HPC) environments. The scheduler aims to effectively schedule batch jobs to optimize a given goal, such as minimizing job wait times or maximizing system utilization, by learning general scheduling rules rather than overfitting a specific workload. The key contributions are: 1) showing deep RL can learn to schedule HPC jobs effectively and the rules generalize, 2) proposing design choices and optimizations to stabilize and accelerate RL training for scheduling, and 3) demonstrating the scheduler can adapt to different optimization goals for autonomous HPC management.
Detailed Information
| Author: | ['pham Kien'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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