AI-based carbon emission reduction for cloud service clients

• Grant number: 23/15949-8
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: April 01, 2024
• End date: March 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Jaime Simão Sichman
• Grantee: Vinicius Renan de Carvalho
• Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

The urgent need to reduce carbon emissions due to global warming has led to innovative approaches in cloud computing. This proposal focuses on the utilization of artificial intelligence, particularly multi-agent systems, evolutionary algorithms and hyper-heuristics, to effectively reduce carbon emissions from the perspective of cloud providers clients. Cloud environments offer an opportunity for cost-effective and sustainable computing, aligning with climate-conscious objectives. The research centers on optimizing cloud resources to minimize carbon emissions, while considering the client's viewpoint, encompassing cost and performance factors. Treating this as a multi-objective optimization problem.The core objectives of this research include investigating cloud scheduling challenges, employing diverse AI algorithms, and comprehensively evaluating their performance. Through this approach, cloud clients can make informed decisions that balance cost-effectiveness, performance, and environmental impact. By applying the power of multi-agent systems, evolutionary algorithms, and hyper-heuristics, this proposal aims to contribute to the sustainable transformation of cloud computing, effectively reducing carbon emissions while ensuring optimal service quality for clients. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE CARVALHO, VINICIUS RENAN; SICHMAN, JAIME SIMAO. HEACT: Hybrid Evolutionary Algorithm for the Multi-region Multi-objective Cloud Task Scheduling Problem. A Study of Workflow Scheduling in AWS EC2. INTELLIGENT SYSTEMS, BRACIS 2024, PT II, v. 15413, p. 16-pg., 2025-01-01. (23/15949-8)