Load balancing in cloud computing with multi-objective survivors optimization

Abstract

Cloud computing platform enables online services for data sharing, storage, and resource utilization to the cloud users. However, the major problem that occurs during cloud access is the server gets underloaded or overloaded affecting the processing time and resulting in the reduced quality of service (QoS). Specifically, the user tasks are allocated among the Virtual Machines (VMs) with diverse lengths, starting times, and processing times. Hence, load balancing is essential for ensuring that all the VMs are utilized appropriately. Consequently, this research proposes multi-objective optimization for load balancing while considering the network parameters such as makespan reduction, balanced CPU utilization, energy consumption minimization and throughput maximization. Specifically, the proposed MO-survivors’ optimization algorithm exploits the multi-objective fitness function considering the QoS constraints for selecting the VMs based on the capacity for achieving the parallel load execution. Further, the proposed algorithm effectively handles the network traffic, offers proper utilization of resources, manages the load capacity, and reduces the overprovision of infrastructure. The experimental outcomes reveals that the proposed MO-survivors’ optimization for load balancing exhibited better performance with 30 VMs attaining an improvement of 1.77 % over TSMGWO in terms of throughput, and attaining the makespan reduction of 223.03 s with TSMGWO. Further, the proposed approach revealed a reduced degree imbalance of 0.012 over TSMGWO and improved the resource utilization by 5.36 % compared to TSMGWO. Moreover, the results reveal the outstanding performance of the proposed MO-survivors optimization over the other existing algorithms used in the analysis.