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, Page 129-152 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2017.63435 | ||||
 View on SCiNiTO | ||||
| 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 | ||||
|
dth: 0px; "> [1] Handbook of Cloud Computing [online]. Available:http://www.springerlink.com/index/10.1007/978-1-4419-6524-0. [2] J. Tsai, J. Fang, J. Chou ,“Optimized task scheduling and resource allocation on cloud computing Environment using improved differential evolution algorithm”, Computers &Operations Research 40, PP. 3045– 3055,2013. [3] A. Soror, U. F. Minhas, A. Aboulnaga, K. Salem, P. Kokosielis, and S. Kamath, "Deploying Database Appliances in the Cloud.," IEEE Data Eng. Bull., vol. 32, No. 1, PP. 13-20, 2009. [4] Y. Yang, et al., " An Algorithm in SwinDeW-C for Scheduling Transaction- Intensive Cost-Constrained Cloud Workflows," Proc. of 4th IEEE International Conference on e-Science, Indianapolis, USA, PP. 374- 375, December 2008. [5] Y.Chawla, M.Bhonsle, “A Study on Scheduling Methods in Cloud Computing”, International Journal of Emerging Trends & Technology in Computer Science, Vol. 1, Issue 3, PP. 12-17, September – October 2012. [6] Amazon EC2. Available:http://aws.amazon.com/ec2/. [7] Google Cloud. Available:https://cloud.google.com/compute/pricing. [8] Al-maamari, Ali, and Fatma A. Omara."Task SchedulingUsing PSO Algorithm in Cloud ComputingEnvironments."International Journal of Grid andDistributed Computing 8.5 (2015): 245-256. -stroke-width: 0px; "> [9] Ali Al-maamari, Fatma A. Omara.” Task Scheduling using Hybrid Algorithm in Cloud Computing Environments” IOSR Journal of Computer Engineering (May – Jun. 2015), PP 96-106. [10] Suraj Pandey, Linlin Wu, Siddeswara Guru, and Rajkumar Buyya. "A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments." Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA ), Perth, Australia. April 20-23, 2010. [11] Ke Liu, Hai Jin, Jinjun Chen, Xiao Liu, Dong Yuan, Yun Yang , " A Compromised-Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost-Constrained Workflows on a Cloud Computing Platform," International Journal of High Performance Computing Applications - IJHPCA , vol. 24, no. 4, pp. 445-456, 2010 [12] J.Huang. "The Workflow Task Scheduling Algorithm Based on the GA Model in the Cloud Computing Environment." Journal of Software, vol. 9, No 4, PP. 873-880, April 2014. [13] Lei Zhang, et al. "A Task Scheduling Algorithm Based on PSO for Grid Computing." International Journal of Computational Intelligence Research, vol. 4, No.1, PP. 37–43, 2008. [14] M.Al-Roomi, S.Al-Ebrahim, S.Buqrais and I.Ahmad,“Cloud Computing Pricing Models: A Survey”,Vol.6, No.5 (2013), pp.93-106, International Journal of Grid and Distributed Computing. [15] J. D. Ullman. Np-complete scheduling problems. J. Comput.Syst. Sci., 10(3), 1975. [16] Visalakshi, P. and S. Sivanandam, Dynamic task scheduling with load balancing using hybrid particle swarm optimization. Int. J. Open Problems Compt. Math, 2009. 2(3): p. 475-488. [17] Selvarani, S. and G.S. Sadhasivam. Improved cost-based algorithm for task scheduling in cloud computing. in Computational intelligence and computing research (iccic), 2010 ieee international conference on. 2010. [18] Uma, S., et al., A hybrid PSO with dynamic inertia weight and GA approach for discovering classification rule in data mining. International Journal of Computer Applications, 2012. 40(17). [19] Girgis, M. R., Mahmoud, T. M., Abdullatif, B. A., & Rabie, A. M. Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Tabu Search Optimization Methods. [20] Elhossiny Ibrahim, Nirmeen A. El-Bahnasawy, Fatma A. Omara, “Job Scheduling based on Harmonization Between The requested and Available Processing Power in The Cloud Computing Environment”, IJCA, Volume 125 – No.13, September 2015. [21] Rajkumar Buyya, Rajiv Ranjan and Rodrigo N. Calheiros, “Modeling and Simulation of Scalable Cloud ComputingEnvironments and the CloudSim Toolkit: Challenges and Opportunities” in the 7th High Performance | ||||
|
Statistics Article View: 123 |
||||
