A Comparative Review of Heuristic vs Metaheuristic Techniques in Cloud Resource Optimization

Abstract

The flexibility, scalability, and on-demand services offered by cloud computing have fundamentally altered the way computer resources are accessed and handled. Nevertheless, dynamicity and heterogeneity of cloud environment present a major problem in the optimization of resources efficiently. A key problem in cloud computing is optimizing cloud resources, which aims to improve performance, reduce operating expenses, and guarantee effective use of computational resources including CPU, memory, storage, network bandwidth, and energy. A thorough review of cloud resource categories and the main distinctions between scheduling and resource allocation procedures is provided in this work. It investigates the use of heuristic approaches that provide quick and easy solutions for resource management in predictable situations, such as Shortest Job First (SJF), Round-Robin (RR), Min-Min, Max-Min, and First-Come-First-Served (FCFS). Additionally, because they offer scalable and reliable optimizations in dynamic and diverse cloud environments, the study highlights metaheuristic algorithms including Simulated Annealing (SA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). A comparative analysis highlights the strengths and limitations of both strategies in terms of computational overhead, accuracy, scalability, and convergence speed. The paper concludes by identifying key future directions, including hybrid models, AI-driven techniques, energy-aware optimization, privacy-preserving strategies, and cloud-edge synergy, to address the evolving demands of next-generation cloud systems.