Load Balancing Scheduling Algorithm in Cloud Computing System with Cloud Pricing Comparative Study | ||
| Menoufia Journal of Electronic Engineering Research | ||
| Article 9, Volume 26, Issue 1, January 2017, Pages 129-152 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjeer.2017.63435 | ||
| Authors | ||
| Elhossiny Ibrahim1; Nirmeen A. El-Bahnasawy1; Fatma A. Omara2 | ||
| 1Dept. of Computer Science and Eng., Faculty of Elect., Eng., Menoufia University | ||
| 2Faculty of Computers & information, Cairo University, Egypt. | ||
| Abstract | ||
| ; "> Cloud Computing is one of the most recent technologies based on virtualization over IT resources where virtual storage and computing services are provided.On the other hands, cloud computing is based on the concepts of virtualization, multitenancy, and shared infrastructure.Task scheduling on the available resources (i.e., Virtual Machines (VMs)) is considered one of the main challenges in cloud computing wherescheduling compliation time (make_span), and execution price of tasks should be minimized.In this paper, a task scheduling algorithm on the Cloud Computing environment has been proposed to reduce the make-span, as well as, decrease the price of executing the independent tasks on the cloud resources.The proposed 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 calculate the power factor of each VM (the ratio of it’s processing power to the total processing power of all VMs ) ,then searching the users' tasks to find a task or a group of tasks that their processing power near to the power factor of each VM. So, fairness is achieved by allocating each VM according it’s power. To evaluate the performance of the proposed algorithm, a comparative study has been done among the proposed algorithm, and the existedGA, and PSO algorithms, The price of the execution has been measured using Amazon and Google pricing models. The experimental results show that the proposed algorithm outperforms other algorithms by reducing make-span and the price of the running tasks on specific resource. | ||
| References | ||
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