Task Scheduling Algorithm in Cloud Computing Environment Based on Cloud Pricing Models

Abstract

The Cloud Computing is a most widely spreading platform for executing tasks using virtual machines (VMs) as processing elements. Therefore, implementing HPC using Cloud Computing is considered a powerful approach by isolating tasks, reducing execution time, as well as, price, and satisfying load balance. In this paper, an enhancement task scheduling algorithm on the Cloud Computing environment has been introduced to reduce the make-span, as well as, decrease the price of executing the independent tasks on the cloud resources. The principles of the algorithm is based on calculating the total processing power of the available resources (i.e., VMs) and the total requested processing power by the users' tasks, then allocating a group of users' tasks to each VM based on the ratio of its needed power relative to the total processing power of all VMs. The power of VMs has been defined based on Amazon EC2 and Google pricing models. To evaluate the performance of the enhancement algorithm, a comparative study has been done among this enhancement algorithm, the default FCFS algorithm, and the existed GA, and PSO algorithms. The experimental results show that the enhancement algorithm outperforms other algorithms by reducing make-span and the price of the running tasks.