Effective load balancing in cloud computing using a hybrid optimization algorithm
2026, vol.18 , no.2, pp. 23-34
Article [2026-02-03]
Effective load balancing is a critical challenge in cloud computing environments, where dynamic and unpredictable workloads necessitate efficient resource allocation to ensure optimal performance and energy efficiency. This paper proposes a hybrid optimization algorithm, combining Quantum-Gray Wolf Optimization (Q-GWO) and Particle Swarm Optimization (PSO) to enhance load balancing in cloud computing. The Q-learning technique enhances the decision-making process by selecting appropriate virtual machines (VMs) based on their predicted fitness values. The hybrid GWO-PSO algorithm is then used to update positions and velocities, ensuring optimal resource utilization. Empirical evaluations demonstrate the superiority of the Q-GWO-PSO algorithm over traditional methods like Q-Learning Modified PSO (QMPSO) and Modified PSO (MPSO). Specifically, Q-GWO-PSO achieved a makespan of 7074.11 milliseconds (ms), throughput of 2129.16 requests per second (req/ms), a Standard Deviation (SD) of 0.05479, energy utilization of 150.979-kilo joule (KJ), and migrated 306 tasks, showcasing its robust and scalable solution for load balancing.
Load balancing, cloud computing, hybrid optimization, VMs
https://doi.org/10.59035/ECFJ5461
Anushree Kaushik, Gulista Khan, Priyank Singhal. Effective load balancing in cloud computing using a hybrid optimization algorithm. International Journal on Information Technologies and Security, vol.18 , no.2, 2026, pp. 23-34. https://doi.org/10.59035/ECFJ5461