An Optimized Cloud Load Balancing Approach Using Hybrid DE-ABC Algorithms

Abstract

Cloud computing is a fast-growing emerging field that users can access a diverse services —such as data storage, software applications, and servers—via the Internet. It enables organizations to utilize remote resources provided by various service providers on a pay-as-you-go basis. This model reduces the need for extensive on-site infrastructure, allowing businesses to manage large-scale data and applications virtually through the cloud. Load balancing is the potential process of assigning or allocating the load among the different virtual machines existing in the data center. The workload entering into the cloud computing environment need to be significantly allocated to the resources, such that each share is responsible for sharing an equal amount of loads at any particular point of time. The performance of the cloud environment completely depends on the degree to which the resources are equally shared, since imbalance in load leads to deterioration in the network efficiency. This proposed work is the detailed view of the DE-ABC-Load Balancing (DE-ABCA-LB) scheme presented for effective and efficient load balancing in cloud computing framework. This study presents a mathematical model along with the parameters used to design the fitness function that supports the DE-ABC-LB approach for effective load balancing among virtual machines in a cloud environment. It also details the experimental setup and analyzes the performance of the suggested work DE-ABC-LB method under varying conditions, including different task volumes, instruction lengths, and numbers of virtual machines