Optimal load balancing in cloud computing using hybrid meta-heuristic algorithms

Abstract

Customers can access applications and storage through the cloud as a service. In the cloud, load balancing (LB) is crucial for distributing tasks between virtual machines (VMs). Utilizing a variety of LB techniques, users submit tasks to the cloud, which are distributed between VMs to speed up operations. LB speeds up VM operations. In the proposed work, two meta-heuristic algorithms, particle swarm optimization (PSO) and genetic algorithm (GA), are hybridized (hybridized GA and PSO (HGAPSO)) to achieve optimal LB. PSO is utilized to choose the global best VM, while the GA aids in selecting the best population from the initial population. The proposed work analyzed the different parameters, such as average processing time (APT), finishing time, reaction time, make-span, throughput, and cost. CloudSim tool with various levels of tasks is used for performance evaluation of different algorithms such as ant colony optimization (ACO), Harris Hawks optimization (HHO), and hybridization of HHO, GA, and PSO. The proposed HGAPSO algorithm demonstrates significant performance improvements over existing algorithms such as GA, PSO, HHO, ACO, and HHO–ACO. Experimental results show that HGAPSO achieves an 88.11% improvement in APT and a 70% reduction in average response time. Moreover, makespan is reduced by 62%, throughput is improved by 4.95%, and computational cost is lowered by 1.58%. These enhancements collectively confirm the efficiency of the proposed approach in achieving balanced load distribution and improved cloud performance.

Keywords

virtual machine genetic algorithm particle swarm optimization cloud computing load balancing

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