Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Abstract

Cloud computing plays a significant role in Information Technology (IT) industry to deliver scalable resources as a service. One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling. The main advantage of this scheduling is to maximize the performance and minimize the time loss. Various researchers examined numerous scheduling methods to achieve Quality of Service (QoS) and to reduce execution time. However, it had disadvantages in terms of low throughput and high response time. Hence, this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines (VMs). For this purpose, the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task (PSO-BATS) scheduling with Multi-Layered Regression Host Employment (MLRHE) to sort out the issues of task scheduling and ease the scheduling operation by load balancing. The proposed efficient scheduling provides benefits to both cloud users and servers. The performance evaluation is undertaken with respect to cost, Performance Improvement Rate (PIR) and makespan which revealed the efficiency of the proposed method. Additionally, comparative analysis is undertaken which confirmed the performance of the intro-duced system than conventional system for scheduling tasks with high flexibility.