# Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration

arXiv: https://arxiv.org/abs/2507.01225 · HTML: https://mlsanity.com/paper/2507.01225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 34 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [Flow Time Scheduling with Uncertain Processing Time](https://mlsanity.com/paper/2103.05604/) (2021)

## Abstract (arXiv)

Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage, and (b) provide high quality-of-service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves much lower peak resource usage compared to manual scheduling without compromising on the quality-of-service.