Effective heuristic task scheduling algorithm for virtual machines in heterogeneous cloud computing

Abstract

In a cloud computing environment, the allocation of virtual machines for executing the user-submitted task is a challenging process. Specifically for large task sizes in the cloud environment, finding an optimal task scheduling solution is regarded as an NP-hard problem. Optimizing the tasks in a virtual machine's data center while reducing important, influential, and cost-effective parameters such as energy usage, makespan, and cost is the best course of action. Hence to minimize these parameters, an effective heuristic algorithm such as the dynamic predator-prey optimization technique is proposed to execute the task scheduling process in the cloud server that assists the virtual machine manager (VMM) to allocate the task in an optimal way that enhances and secures the performance. While the physical machine consumes more data and space the proposed method utilizes a Virtual Machine (VM) for task scheduling where the VMM holds the responsibility for scheduling the task for VMs. The dynamic predator-prey optimization is a behavioral combination of grey wolf optimization and sparrow search optimization where the foraging behavior assists in finding the solution in a large search space and the hunting behavior of grey wolf optimization is integrated. The overall combined behavior helps the VMM in scheduling the task to the correct VM considering the capacity and capability to perform the task. The dynamic predator-prey optimization method attains a makespan of 794.97 s, a throughput of 1 bps, a degree of imbalance is 0.180 and an Average Resource Utilization Ratio is 0.95. The result findings state that the dynamic predator-prey optimization obtains the best results in allocating the tasks for the VM.