Performance Analysis of the Normalized Distribution and Ranking with Optimization Based Task Scheduling Techniques

Abstract

Cloud computing is becoming more popular as a way to pay for IT services. Many IT service providers use cloud computing in their day-to-day operations. Mood services are located at swing locations in cloud computing. Because of the system's regional spread, operating actions, and heterogeneity of resources, resource supervision and scheduling become a hidden claim. User satisfaction is increased by completing cloud computing scheduling. In cloud computing, efficient task scheduling reduces the time it takes to get a system up and running. The client's requirement for QoS is the key motivator for task scheduling. The task with the high QoS requirement is scheduled after the task with the low QoS requirement. Users have enough resources to pay for facilities depending on utilization period, so the aim of task scheduling is to reduce costs by shortening the makespan era. A comparison of task scheduling algorithms using optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm is presented in this paper (WOA). In addition, for reducing the makespan in a cloud setting, this paper proposed a Normalized Distribution and Ranking task scheduling method.