Evolutionary job scheduling with optimized population by deep reinforcement learning

2021 ◽  
pp. 1-16
Author(s):  
Detian Zeng ◽  
Jun Zhan ◽  
Wei Peng ◽  
Zengri Zeng
Keyword(s):  
Reinforcement Learning ◽  
Job Scheduling

scholarly journals RLSchert: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction

2021 ◽  
Vol 11 (20) ◽  
pp. 9448
Author(s):  
Qiqi Wang ◽  
Hongjie Zhang ◽  
Cheng Qu ◽  
Yu Shen ◽  
Xiaohui Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
High Performance ◽  
Job Scheduling ◽  
Vital Role ◽  
Runtime Prediction ◽  
Heuristic Scheduling ◽  
The Status ◽  
Computing Platforms ◽  
Job Scheduler ◽  
Policy Optimization

The job scheduler plays a vital role in high-performance computing platforms. It determines the execution order of the jobs and the allocation of resources, which in turn affect the resource utilization of the entire system. As the scale and complexity of HPC continue to grow, job scheduling is becoming increasingly important and difficult. Existing studies relied on user-specified or regression techniques to give fixed runtime prediction values and used the values in static heuristic scheduling algorithms. However, these approaches require very accurate runtime predictions to produce better results, and fixed heuristic scheduling strategies cannot adapt to changes in the workload. In this work, we propose RLSchert, a job scheduler based on deep reinforcement learning and remaining runtime prediction. Firstly, RLSchert estimates the state of the system by using a dynamic job remaining runtime predictor, thereby providing an accurate spatiotemporal view of the cluster status. Secondly, RLSchert learns the optimal policy to select or kill jobs according to the status through imitation learning and the proximal policy optimization algorithm. Extensive experiments on real-world job logs at the USTC Supercomputing Center showed that RLSchert is superior to static heuristic policies and outperforms the learning-based scheduler DeepRM. In addition, the dynamic predictor gives a more accurate remaining runtime prediction result, which is essential for most learning-based schedulers.

A novel multi-agent reinforcement learning approach for job scheduling in Grid computing

2011 ◽  
Vol 27 (5) ◽  
pp. 430-439 ◽  
Author(s):  
Jun Wu ◽  
Xin Xu ◽  
Pengcheng Zhang ◽  
Chunming Liu
Keyword(s):  
Reinforcement Learning ◽  
Grid Computing ◽  
Job Scheduling ◽  
Learning Approach ◽  
Multi Agent

Cost-aware job scheduling for cloud inutances using deep reinforcement learning

2021 ◽  
Author(s):  
Feng Cheng ◽  
Yifeng Huang ◽  
Bhavana Tanpure ◽  
Pawan Sawalani ◽  
Long Cheng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Job Scheduling
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