{"ID":2899838,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.01225","arxiv_id":"2507.01225","title":"Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration","abstract":"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.","short_abstract":"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 capacit...","url_abs":"https://arxiv.org/abs/2507.01225","url_pdf":"https://arxiv.org/pdf/2507.01225v2","authors":"[\"Sunandita Patra\",\"Mehtab Pathan\",\"Mahmoud Mahfouz\",\"Parisa Zehtabi\",\"Wided Ouaja\",\"Daniele Magazzeni\",\"Manuela Veloso\"]","published":"2025-07-01T22:56:08Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}