| Full text | |
| Author(s): |
de Carvalho, Vinicius Renan
;
Sichman, Jaime Simao
Total Authors: 2
|
| Document type: | Journal article |
| Source: | INTELLIGENT SYSTEMS, BRACIS 2024, PT II; v. 15413, p. 16-pg., 2025-01-01. |
| Abstract | |
Cloud computing has revolutionized the provisioning and access of computing resources, offering scalable and flexible alternatives to traditional infrastructure. However, defining how to use these computational resources may be challenging. This paper addresses the challenge of workflow scheduling in cloud environments, focusing on Amazon Web Services (AWS) Elastic Compute Cloud (EC2). We present HEACT, a novel approach that integrates a multi-objective evolutionary algorithm with a specialist scheduling heuristic. The evolutionary algorithm is responsible for generating an initial set of machines (with their performance capability and cost information). The set is sent to the specialist scheduling heuristic for efficient task assignment in these machines. Our approach considers fourteen AWS regions, accurate pricing information from AWS, and employs SimGrid to simulate task execution. The proposed method was benchmarked considering established heuristics (HEFT, PEFT, HSIP, MPEFT) and meta-heuristics (NSGA-II, AGEMOEA2). Results demonstrated that the combinations of AGEMOEA2 with MPEFT and AGEMOEA2 with HEFT yield the best performance, indicating AGEMOEA2's efficacy as a state-of-the-art meta-heuristic for workflow scheduling. (AU) | |
| FAPESP's process: | 23/15949-8 - AI-based carbon emission reduction for cloud service clients |
| Grantee: | Vinicius Renan de Carvalho |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |