Comparative Performance Analysis of Task Scheduling In Cloud Computing Using ACO, PSO, Firefly And Loa Algorithms

Abstract

Cloud computing is a client-driven requirement that provides many resources with the goal of sharing them as a service over the internet. It is a stage in which Cloud clients can access a unique pool of resources and virtualization. Cloud computing provides dynamic resource allocation on demand, as well as scalability, availability, and various services. The cloud provides services to organizations such as processing, storage, server, and applications that are located in remote areas by utilising cloud servers. The system's performance suffers if tasks are not properly scheduled As a result, resource allocation is critical to improving overall system performance. The scheduling of user tasks is critical for improving the performance of cloud services. Cloud providers must effectively schedule their resources for maximum utilisation and user satisfaction.In this paper has given a optimize the scheduling algorithm based on Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO),Firefly Algorithm, Lion Optimization Algorithm (LOA) to improve the performance of cloud task scheduling.  The simulation results show the effectiveness of the comparison by using research parameters like Cost, Resource Utilization, Make span, Execution Time and Completion Time.