.png){kind=icon}
.png){kind=icon}
Return
Duration-Informed Workload Scheduler
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-07612-0_1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 executiona 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 systems perspective.
Keywords:
High-Performance Computing
Duration Prediction
Machine Learning
Journal
H
IF:
0
Papers:
54
Citations:
0