TASA: A New Task Scheduling Algorithm in Cloud Computing
محورهای موضوعی : Cloud, Cluster, Grid and P2P ComputingSomayeh Taherian Dehkordi 1 , Vahid Khatibi Bardsiri 2
1 - Department of Computer Engineering. Kerman Branch. Islamic Azad University. Kerman. Iran
2 - Department of Computer Engineering. Kerman Branch. Islamic Azad University. Kerman. Iran
کلید واژه: cloud computing, Sufferage, Task scheduling algorithm, Min-Min,
چکیده مقاله :
Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. It merges a lot of physical resources and offers them to users as services according to service level agreement. Therefore, resource management alongside with task scheduling has direct influence on cloud networks’ performance and efficiency. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This paper studies the existing approaches of task scheduling and resource allocation in cloud infrastructures and assessment of their advantages and disadvantages. Afterwards, a compound algorithm is presented in order to allocate tasks to resources properly and decrease runtime. The proposed algorithm is built according to conditions of compounding Min-min and Sufferage algorithms. In the proposed algorithm, task allocation between machines takes place alternatively and with continuous change of scheduling algorithms. The main idea of the proposed algorithm is to concentrate on the number of tasks instead of the existing resources. The simulation results reveal that the proposed algorithm can achieve higher performance in decreasing response time.
[1] F. Durao, S.F.J. Carvalho, A. Fonseka and C.V. Garcia, “A systematic review on cloud computing“, The Journal of Supercomputing, Springer US, Vol. 68, 2014, pp.1321-1346.
[2] W. Mingxin, “Research on Improvement of Task Scheduling Algorithm in Cloud Computing“, Applied Mathematics & Information Sciences An International Journal, Vol.9,2015, pp. 507-516.
[3] T. Ma, Y. Chu, L. Zhao, and O. Ankhbayar, “Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm”, IETE Technical Review, Publishing models and article dates explained, Vol.31, 2014, pp.1-16.
[4] R. Kaur, and S. Kinger, “Analysis of Job Scheduling Algorithms in Cloud Computing” , International Journal of Computer Trends and Technology (IJCTT) , Vol.9, 2014, pp.379-386.
[5] S.V.Nandgaonkar, and A.B. Raut, “A Comprehensive Study on Cloud Computing”, International Journal of Computer Science and Mobile Computing, a Monthly Journal of Computer Science and Information Technology,Vol.3,2014, pp.733-738.
[6] R. Mittal, and k. Soni, “Analysis of Cloud Computing Architectures”, International Journal of Advanced Research in Computer and Communication Engineering,Vol.2, 2013, pp.2087-2091.
[7] T. Buchert, C. Ruiz, L. Nussbaum, And O. Richard, “A survey of general-purpose experiment management tools for distributed systems“, Future Generation Computer Systems, Vol.45, 2015, pp. 1-12.
[8] T. Mathew, K.C. Sekaran, and J. Jose, “Study and analysis of various task scheduling algorithms in the cloud computing environment“ , Advances in Computing, Communications and Informatics (ICACCI), International Conference, 2014, pp. 658- 664.
[9] S. Nagadevi, K. Satyapriya, and D. Malathy, “A Survey on Economic Cloud Schedulers for Optimized task scheduling“, International Journal of Advanced Engineering Technology, Vol.5, 2013, pp. 58-62.
[10] L. Tripathy, and R.R. Patra, “Scheduling in Cloud Computing“, International Journal on Cloud Computing: Services and Architecture (IJCCSA), Vol. 4, 2014, pp. 21-27.
[11] C.W. Tsai, and J.P.C. Rodrigues, “Metaheuristic Scheduling for Cloud: A Survey“, Systems Journal, IEEE, Vol.8, 2013, pp. 279-291.
[12] R. Nallakumar, N. Sengottaiyan, and S.Nithya, “A Survey of Task Scheduling Methods in Cloud Computing“. International Journal of Computer Sciences and Engineering, Vol.2, 2014, pp. 9-13.
[13] S. Parsa, and E.R. Maleki, “RASA: A New Task Scheduling Algorithm in Grid Environment“, World Applied Sciences Journal 7 (Special Issue of Computer & IT), 2009, pp. 152-160.
[14] A. Ghorbannia, M. Javanmard, M. Barzegar, and M. Khosravi, “RSDC (Reliable Scheduling Distributed in Cloud Computing) “, International Journal of Computer Science, Engineering and Applications (IJCSEA). Vol.2, 2012, pp. 1-16.
[15] F. Ramezani, L. Jie, and J. Hussain, “Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization“, Springer-Verlag Berlin Heidelber, 2013, pp. 237-251.
[16] J. Elayaraja, and S. Dhanasekar, “A Survey on workflow scheduling in cloud using ant colony optimazation“, International Journal of Computer Science and Mobile Computing, Vol.3, 2014, pp. 39- 44.
[17] X. Xu, N. Hu, and W.Q. Ying, “Cloud Task and Virtual Machine Allocation Strategy Based on Simulated Annealing-Genetic Algorithm“, Applied Mechanics and Materials, Applied Science, Materials Science and Information Technologies in Industry, 2014, pp. 391-394.
[18] M.G. Huang, and Z.Q. Ou, “Review of Task Scheduling Algorithm Research in Cloud Computing“, Advanced Materials Research, Progress in Applied Sciences, Engineering and Technology , 2014, pp. 3236-3239.
[19] k. Gupta, and M. Singh, “Heuristic Based Task Scheduling In Grid“, International Journal of Engineering and Technology (IJET), Vol.4, 2014, pp. 254-260.
[20] H. Suo, H. Yan, “Research on Resource Scheduling in Cloud Computing: Issues and Solutions“, Applied Mechanics and Materials, Numerical Methods, Computation Methods and Algorithms for Modeling, Simulation and Optimization, Data Mining and Data Processing, 2014, pp. 1801-1804.
