# Duration-Informed Workload Scheduler

arXiv: https://arxiv.org/abs/2604.09599 · HTML: https://mlsanity.com/paper/2604.09599/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 7 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Leakage-Safe and Scheduler-Aware Machine Learning for Grid Job Runtime Prediction](https://mlsanity.com/paper/2609.13701/) (2026)

## Abstract (arXiv)

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.