Matching Computing Requirements of Stochastic Optimization Models and Cloud Computing Resources
Abstract
Cloud computing offers scalable solutions for scientific computing, but efficiently allocating resources for stochastic optimization models remains challenging. This work uses real execution data from an energy sector company to develop machine learning models that predict execution time based on algorithm parameters and cloud infrastructure configurations. To optimize resource usage, we propose a utility-based framework that balances execution time and cloud costs. Our results highlight key factors affecting computational efficiency and provide insights for cost-effective resource provisioning, improving cloud utilization for stochastic optimization applications.References
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Butler, D. (2006). Amazon puts network power online. Nature, 444:528.
Delimitrou, C. and Kozyrakis, C. (2014). Quasar: Resource-efficient and qos-aware cluster management. In Conf. Architectural Support for Programming Lang. and Operating Systems (ASPLOS), pages 127–144.
Menache, I., Shamir, O., and Jain, N. (2014). On-demand, spot, or both: Dynamic resource allocation for executing batch jobs in the cloud. In ICAC, pages 177–187.
Pereira, M. V. and Pinto, L. M. (1991). Multi-stage stochastic optimization applied to energy planning. Mathematical programming, 52:359–375.
Perez-Salazar, S., Menache, I., Singh, M., and Toriello, A. (2022). Dynamic resource allocation in the cloud with near-optimal efficiency. Operations Research, 70(4):2517–2537.
Tesauro, G. (2007). Reinforcement learning in autonomic computing: A manifesto and case studies. IEEE Internet Computing, 11(1):22–30.
Zhang, W., Li, M., and Chen, X. (2024). Quality-aware resource allocation in cloud computing: A machine learning approach. IEEE Transactions on Cloud Computing, 12(1):34–47.
Published
2025-07-20
How to Cite
BERNARDINO, Pedro H.; MENASCHE, Daniel Sadoc; PEREIRA, Mario Veiga.
Matching Computing Requirements of Stochastic Optimization Models and Cloud Computing Resources. In: WORKSHOP ON PERFORMANCE OF COMPUTER AND COMMUNICATION SYSTEMS (WPERFORMANCE), 24. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 13-24.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2025.8294.
