A Hybrid ACO-GA Model for High-Quality Resource Allocation

• Author(s): Lesson, W.A.; Akazue, M.I.
• Paper ID: 1715299
• Page: 1806-1815
• Published Date: 23-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026

Abstract

The distribution of resources is a challenging issue due to the dynamic nature of operations, the diversity of resources, and the varying priorities of activities and applications. Allocating resources involves giving virtual machines the characteristics that clients have chosen. Effective use of cloud resources also includes managing workloads and assigning them to virtualization. Allocating resources is one of cloud computing's most important duties. The different resource allocation paradigms and cost-based resource allocation method for the heterogeneous cloud environment are examined in this research. Site visits and document review were the analysis tools employed to collect data. A review of current methods for allocating resources was examined. The models were evaluated based on the algorithms' strengths, weaknesses, and functionality. The results demonstrated that the ACO-GA model hybridization offered a high-quality resource allocation in cloud computing.

Keywords

Resource Allocation, Ant Colony, Genetic Algorithm, Optimization, Cloud Computing; Hybridization.

Citations

IRE Journals:
Lesson, W.A., Akazue, M.I. "A Hybrid ACO-GA Model for High-Quality Resource Allocation" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 1806-1815 https://doi.org/10.64388/IREV9I9-1715299

IEEE:
Lesson, W.A., Akazue, M.I. "A Hybrid ACO-GA Model for High-Quality Resource Allocation" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715299