A Comprehensive Evaluation of Swarm Intelligence Metaheuristics for Efficient Load Balancing in Cloud Computing

Cloud computing has transformed resource provisioning across distributed infrastructures, introducing both opportunities and challenges for efficient resource management.A primary challenge in resource management is load balancing, which seeks to optimize overall system performance by intelligently mapping user workloads to available computing resources.Due to the combinatorial complexity in dynamic cloud environments, heuristic and metaheuristic approaches have become essential.Among these, nature-inspired metaheuristic algorithms, which are modeled on collective behaviors observed in nature, have demonstrated strong potential for addressing load-balancing problems.This paper provides a detailed review and performance comparison of popular nature-inspired swarm intelligence algorithms, including Ant Colony Optimization, Particle Swarm Optimization, the Firefly Algorithm, and the Bat Algorithm.Each algorithm was implemented and evaluated within the CloudSim simulation framework under varying cloudlet workloads.Performance was evaluated using key performance metrics related to load-distribution efficiency.The analysis identifies the strengths and limitations of each algorithm, providing evidence-based insights into their suitability for various cloud computing scenarios.Results demonstrate that nature-inspired algorithms hold considerable promise for enhancing load-balancing efficiency of cloud computing environments.