Hybrid Prairie Dog and Beluga Whale optimization algorithm for multi-objective load balanced-task scheduling in cloud computing environments

The cloud computing technology is utilized for achieving resource utilization of remote-based virtual computer to facilitate the consumers with rapid and accurate massive data services. It utilizes on-demand resource provisioning, but the necessitated constraints of rapid turnaround time, minimal execution cost, high rate of resource utilization and limited makespan transforms the Load Balancing (LB) process-based Task Scheduling (TS) problem into an NP-hard optimization issue. In this paper, Hybrid Prairie Dog and Beluga Whale Optimization Algorithm (HPDBWOA) is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment. This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management. It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account. It addresses the problem of pre-convergence with well-balanced exploration and exploitation to attain necessitated Quality of Service (QoS) for minimizing the waiting time incurred during TS process. It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state. The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation. The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput, system, and response time.