Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.09599 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911583960039424 |
|---|---|
| author | Loreti, Daniela Leone, Davide Borghesi, Andrea |
| author_facet | Loreti, Daniela Leone, Davide Borghesi, Andrea |
| contents | High-performance computing systems are complex machines whose behaviour is governed by the correct functioning of its many subsystems. Among these, the workload scheduler has a crucial impact on the timely execution of the jobs continuously submitted to the computing resources. Making high-quality scheduling decisions is contingent on knowing the duration of submitted jobs before their execution--a non-trivial task for users that can be tackled with Machine Learning.
In this work, we devise a workload scheduler enhanced with a duration prediction module built via Machine Learning. We evaluate its effectiveness and show its performance using workload traces from a Tier-0 supercomputer, demonstrating a decrease in mean waiting time across all jobs of around 11%. Lower waiting times are directly connected to better quality of service from the users' point of view and higher turnaround from the system's perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09599 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Duration-Informed Workload Scheduler Loreti, Daniela Leone, Davide Borghesi, Andrea Distributed, Parallel, and Cluster Computing Artificial Intelligence High-performance computing systems are complex machines whose behaviour is governed by the correct functioning of its many subsystems. Among these, the workload scheduler has a crucial impact on the timely execution of the jobs continuously submitted to the computing resources. Making high-quality scheduling decisions is contingent on knowing the duration of submitted jobs before their execution--a non-trivial task for users that can be tackled with Machine Learning. In this work, we devise a workload scheduler enhanced with a duration prediction module built via Machine Learning. We evaluate its effectiveness and show its performance using workload traces from a Tier-0 supercomputer, demonstrating a decrease in mean waiting time across all jobs of around 11%. Lower waiting times are directly connected to better quality of service from the users' point of view and higher turnaround from the system's perspective. |
| title | Duration-Informed Workload Scheduler |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2604.09599 |