Citation :

Khaja Kamaluddin, 2023. "Resource Allocation Optimization in Public Cloud Using Genetic Algorithms: A Performance-Cost Trade-off Analysis" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 1, Issue 3: 190-200.

Abstract :

Resource allocation in public cloud environments requires balancing performance metrics such as execution time and SLA compliance with cost-related factors like resource pricing and utilization. This review examines the effectiveness of Genetic Algorithms (GAs) in addressing this performance–cost trade-off. Gases, with their population-based evolutionary approach, are shown to be well-suited for multi-objective optimization, offering diverse, near-optimal solutions under dynamic cloud workloads. The article discusses key modeling strategies, fitness functions, and the role of GA variants such as NSGA-II in generating Pareto-optimal allocations. A comparative analysis with other metaheuristics, including PSO and ACO, highlights GA's advantages in scalability, adaptability, and trade-off management. The review concludes by identifying research gaps and proposing directions for future work, including hybrid and real-time adaptive GA-based cloud schedulers.

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Keywords :

Resource Allocation, Optimization, Cloud Computing.