HSSAGWO Scheduler for Efficient Task Scheduling in an IaaS Cloud Computing Environment

Innovations in cloud technology over the recent years have shown tremendous growth.Apart from the need to possess a basic internet connection, another major hiccup for researchers in cloud computing is load balancing.It refers to the way resources are distributed and tasks are performed to achieve the most optimal utilization.Effective load balancing provides more user satisfaction.There are many algorithms developed to tackle the challenge of Load Balancing.An attempt is made in this paper to provide solution to this issue by developing an optimization technique that efficiently regulates the scheduler in assigning tasks to cloud resources such that optimal results are obtained.A hybridized Sparrow Search Algorithm -Grey Wolf Optimizer (HSSAGWO) had been proposed to optimize the task scheduling activity in cloud.The exploration and exploitation activities that are a part of original algorithms have been refined to achieve better performance in the proposed HSSAGWO algorithm.The performance efficiency of HSSAGWO algorithm had been ascertained by comparing it with Sparrow Search Algorithm (SSA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Particle Swarm Optimization (PSO).Simulated experiments had been conducted using Cloudsim 3.0 tool for obtaining the results.The performance comparison had been carried out by considering the makespan, cost and response time parameters.The proposed HSSAGWO technique had produced an improvement of 9.31%,12.23%,15.55% and 17.95% for makespan when compared with SSA, GWO, GSA and PSO algorithms respectively when arrival rate is 10.