Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Abstract

AbstractCloud computing is the most rapidly growing technology that facilitates virtualized computer resources to the users through its service providers. Load balancing and task scheduling is the important concern in the cloud computing scenario from the service provider point of view. The process of load balancing is responsible for allocating the dynamic workload among the cloud systems, such that equal sharing of the resources without the virtual machines (VMs) being overloaded or underloaded. This load balancing process not only needs to allocate the tasks to suitable VMs but also need to maintain trade‐off among VMs. Thus, a potential dynamic and optimized load balancing strategy is essential for improving resource utilization, services, reliability, and throughput by maintaining equilibrium between VMs with minimized makespan. In this article, hybrid dingo and whale optimization algorithm‐based load balancing mechanism (HDWOA‐LBM) is proposed for effective load balancing those aids in maximized throughput, reliability, and resource utilization in the clouds. This HDWOA‐LBM is proposed by mimicking the hunting characteristics of dingo (equivalent to tasks) and VMs as their prey to be hunted. It inherited the process of exploration and exploitation through the chasing and approaching, encircling, and harassing inherent with dingo optimization algorithm (DOA) to determine the optimal allocation of incoming tasks to the suitable VM. In specific, it utilized the merits of whale optimization for improving the exploitation phase of DOA to balance the trade‐off between local and global search. The simulation experiments of the proposed HDWOA‐LBM conducted using CloudSim confirmed better throughput of 21.28%, maximized reliability of 25.42%, minimized makespan of 22.98%, and improved resource allocation of 20.86%, on par with the competitive intelligent load balancing schemes used for investigation.