Multi Objective Prairie Dog Optimization Algorithm for Task Scheduling and Load Balancing

Abstract

An efficient task scheduling plays an important role in facilitating the virtual resource in a cloud computing environment by minimalizing make span and enhancing the allocation of resources. Requests for resources are treated as tasks, and appropriate resources are allocated based on user requirements. But, due to high demand and requests, the cloud has difficulty allocating resources. To overcome the issues, this research introduced an optimization-based task scheduling approach. The Multi-Objective Prairie Dog Optimization (MOPDO) algorithm is introduced which considers the makespan time and the execution time as the major objective while allocating resources in IoT. The proposed MOPDOA effectively allocates the resource to the Virtual Machines (VMs) by choosing the host with maximized resources. The search mechanism with the help of MOPDO helps to detect a suitable VM for resource allocation will be continued. After the process of allotting the resources to VMs, the load balancing process must be initiated to schedule the tasks for VMs. When the task count is assigned as 100, the makespan time of MOPDOA is 12s while Particle Swarm Gray Wolf Optimization (PSGWO) obtains a makespan time of the 20s. Similarly, for different VMs, the proposed approach is 175.45s for execution whereas the existing Improved Multi-Objective Multi-Verse Optimizer obtains 186.33s to execute for 10 VMs.