An Efficient Job Scheduling Strategy for Multi cloud Environment

Abstract

Cloud computing has revolutionized the information and communication technology field by providing dynamic and highly scalable resources on demand to end users on a pay as you go model It reduces the high upfront investment cost for infrastructure and maintenance and upgrades cost by allowing the organization to either outsource its computational needs or provide a cloud server independent job scheduling advancement and provisioning processes in the cloud There are many computing fields like a grid distribute cluster and cloud that aims to provide computational power as a utility to many end users Cloud computing has revolutionized IT technology providing services to users and consumers through the internet Many challenges are facing the cloud computing system and one of the most important and prominent challenges is the task scheduling problem Job scheduling tackles assigning jobs that come from the user to the appropriate resources provided by the service provider According to the Quality of services agreed in the service level agreement some restrictions such as deadline and budget are the major constraints in QoS applied on the users requirements of the cloud resources that means the service provider commits to perform not only the tasks within a specified time Job allocation for heterogeneous cloud scheduling is encoded as a process of brainstorming In addition the resulting scheduling scheme is evaluated for different performance constraints such as resource utilization rate job completion and the span and outcomes are verified Next the proposed model is compared with Brain Storm Optimization Particle swarm optimization Genetic Algorithm and Differential Evolution and the analysis demonstrates its improved performance In addition the statistical assessment of the performance of these algorithms is based on three parameters like resource utilization makespan and cost newline newline newline