Work

Matching Computing Requirements of Stochastic Optimization Models and Cloud Computing Resources

Year: 2025

Type: conference-paper

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.

DOI: 10.5753/wperformance.2025.8294

Authors: Pedro H. Bernardino, Daniel Sadoc Menasche, Mario Veiga Pereira

Institutions: Universidade Federal do Rio de Janeiro, Pacific School of Religion

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

OpenAlex ID: W4412827075

FWCI:

Cites: 7

Cited by:

Related to: 10