An Improved Performance of Task Scheduling Algorithm in Cloud Computing With Optimization

B. Uma, B. Sony, R. Padmini .


In cloud computing datacenters exert server unification to enhance the efficiency of resources. Many Vms (virtual machine) are running on each datacenter to utilize the resources efficiently. Most of the time cloud resources are underutilized due to poor scheduling of task (or application) in datacenter. In this paper, we propose a multi-objective task scheduling algorithm form applying tasks to a Vms in order to improve the throughput of the datacenter and reduce the cost without violating the SLA (Service Level Agreement) for an application in cloud SaaS environment. The proposed algorithm provides an optimal scheduling method. Most of the algorithms schedule tasks based on single criteria (i.e execution time). But in cloud environment it is required to consider various criteria like execution time, cost, bandwidth of user etc. This algorithm is simulated using CloudSim simulator and the result shows better performance and improved throughput.

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Shamsollah Ghanbari, Mohamed Othman “A Priority based Job SchedulingAlgorithm in Cloud Computing”, International Conference on Advances Scienceand Contemporary Engineering 2012 (ICASCE 2012).

El-Sayed T. El-kenawy, Ali Ibraheem El-Desoky, Mohamed F. Al-rahamawy“Extended Max-Min Scheduling Using Petri Net and Load Balancing,”International Journal of Soft Computing and Engineering (IJSCE) ISSN:2231-2307, Volume-2, Issue-4, September 2012.

ShalmaliAmbike, DiptiBhansali, JaeeKshirsagar, JuhiBansiwal “AnOptimistic Differentiated Job Scheduling System for Cloud Computing,”International Journal of Engineering Research and Applications (IJERA)ISSN: 2248-9622 Vol. 2, Issue 2,Mar-

SandeepTayal “Task Scheduling optimization for the Cloud Computing Systems”,”(IJAEST) International journal of advanced engineering sciences andtechnologies Vol No. 5, Issue No. 2, 111 115.

Jianfeng Zhao; WenhuaZeng; Min Liu; Guangming Li; Min Liu, “Multiobjective optimization model of virtual resources scheduling under cloud computing and it’s solution,”Cloud and Service Computing (CSC), 2011 International Conference on , vol., no., pp.185,190, 12-14

Wei-JenWang, Yue-Shan Chang*, Win-Tsung Lo, and Yi-Kang Lee, “AdaptiveScheduling for Parallel Tasks with QoS Satisfaction for Hybrid Cloud Environments,”Journal of Supercomputing, DOI: 10.1007/s11227-013-0890-2. (SCI)2013.

Achar, R.; Thilagam, P.S.; Shwetha, D.; Pooja, H.; Roshni; Andrea,“Optimal scheduling of computational task in cloud using Virtual MachineTree,”Emerging Applications of Information Technology (EAIT), 2012 ThirdInternational Conference on , vol., no., pp.143,146, Nov. 30 2012-Dec. 1 2012.

“Non-Dominated rting” optimization.

Das, A.K.; Adhikary, T.; Razzaque, M.A.; ChoongSeon Hong, “An intelligent approach for virtual machine and QoS provisioning in cloud Computing,”Information Networking (ICOIN), 2013 International Conference on , vol.,no., pp.462,467, 28-30 Jan. 2013.

Moses, J.; Iyer, R.; Illikkal, R.; Srinivasan, S.; Aisopos, K., “Shared Resource Monitoring and Throughput Optimization in Cloud-Computing Datacenters,”Parallel & Distributed Processing Symposium (IPDPS), 2011 IEEEInternational , vol., no., pp.1024,1033, 16-20 May 2011.

Hilda Lawrance, Dr.Salaja Silas “Efficient QoS Based Resource SchedulingUsing PAPRIKA Method for Cloud Computing,”International Journal of Engineering Science and Technology (IJEST) Vol. 5, No.03 pp 638-643 March2013.

C. Lin, S. Lu, “Scheduling Scientific Workflows Elastically for Cloud Computing,”in IEEE 4th International Conference on Cloud Computing, pp. 246-247,2011.

Mrs.S.Selvarani1; Dr.G.SudhaSadhasivam, “Improved cost-based algorithm fortask scheduling in Cloud computing,”IEEE 2010.

Raju, R.; Babukarthik, R.G.; Chandramohan, D.; Dhavachelvan, P.; Vengattaraman, T., “Minimizing the make span using Hybrid algorithm for cloud computing,”Advance Computing Conference (IACC), 2013 IEEE 3rd International, vol., no., pp.957,962, 22-23 Feb. 2013.

R.Gogulan, .A.Kavitha, U.Karthick Kumar “An Multiple Pheromone Algorithm for Cloud Scheduling With Various QoSRequirements,”IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 3, No 1, May 2012.

Nguyen, QuyetThang; Quang-Hung, Nguyen; Tuong, Nguyen Huynh; Tran,Van Hoai; Thoai, Nam, “Virtual machine allocation in cloud computing for minimizing total execution time on each machine,”Computing, Management andTelecommunications (ComManTel), 2013 International Conference on , vol.,no., pp.241,245, 21-24 Jan. 2013.

Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose,and RajkumarBuyya, “CloudSim: A Toolkit for Modeling and Simulationof Cloud Computing Environments and Evaluation of Resource ProvisioningAlgorithms,”Software: Practice and Experience (SPE), Volume 41, Number 1,Pages: 23-50, ISSN: 0038-0644, Wiley Press, New York, USA, January, 2011.

I. Foster, Y. Zhao, I. Raicu, and S. Lu.“Cloud computing and grid computing 360-degree com-pared.”In Proceedings of Grid Computing EnvironmentsWorkshop, pages 1—10, 2008.

D. M. S. Daryl C. Plummer, David W. Cearley. “Cloud computing confusionleads to opportunity. Technical report,”Gartner Research, 2008.

P. Mell and T. Grance.The NIST Definition of Cloud Computing (Draft).National Institute of Standards and Technology, 53:7, 2010.

Cloud computing and distributed computing.

Tasks Scheduling(computing) (computing)


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