PSO-JSO: A Hybrid Metaheuristic for Load Balancing in Cloud Computing
Abstract
Cloud computing platforms face growing challenges in efficiently allocating resources and balancing loads due to the dynamic and heterogeneous nature of workloads. This work introduces PSOJSO, an innovative hybrid optimization algorithm that fuses Particle Swarm Optimization (PSO) and Jellyfish Search Optimization (JSO). It presents a dynamic time-control mechanism and adaptive coefficients to balance global exploration and exploitation. Experiments were simulated under a time-sharing scheduling policy using CloudSim 3.0.2 for 2 data centers, eight virtual machines, and 10–100 cloudlets. PSOJSO is compared against five baseline algorithms: ACO, ABC, BA, CSA, and PSO alone. PSOJSO achieved a reduction of up to 25.3% in makespan, a 19.8% reduction in energy consumption, and a 17.5% enhancement in resource utilization, proving its validity for dynamic cloud environments.
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DOI: https://doi.org/10.31449/inf.v49i11.8670
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