Name
Title | ||
|---|---|---|
Reinforcement Learning based Fragment-Aware Scheduling for High Utilization HPC Platforms |
Abstract | ||
|---|---|---|
Due to high capacity and complex scheduling activities, a HPC platform often creates resource fragments with low usability. This paper develops a novel fragment-aware scheduling approach which improves system utilization by fitting elastic lightweight tasks to the fragments of resources dynamically. The new approach employs a threshold to determine the balancing factor between the length of tasks and the degree of granularity of the resource fragments. We employ the PPO reinforcement learning approach to train a neural network that can compute the threshold precisely. With the threshold that is adaptive to the changing system states, the PPO-based scheduler is able to utilize the idle resources and maximize the execution success rate of the tasks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TAAI48200.2019.8959932 | 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI) |
Keywords | DocType | ISSN |
High-performance computing,malleable task,reinforcement learning,scheduling | Conference | 2376-6816 |
ISBN | Citations | PageRank |
978-1-7281-4667-6 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lung-Pin Chen | 1 | 0 | 0.34 |
| I-Chen Wu | 2 | 208 | 55.03 |
| Yen-Ling Chang | 3 | 0 | 0.34 |