Document Type: Original Research Paper

Authors

Department of Computer Engineering. Kerman Branch. Islamic Azad University. Kerman. Iran

Abstract

Since software systems play an important role in applications more than ever, the security has become one of the most important indicators of softwares.
Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This research 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. In this paper we proposed a new method for task scheduling by learning automata (LA). This method where has named RAOLA is trained by historical information of task execution on the cloud, then divide task to many classes and evaluate them. Next, manage virtual machine for capture physical resources at any period based on rate of task classes, such that improve efficiency of cloud network.

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[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. 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.
[4] 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.
[5] 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.
[6] F. Ramezani, L. Jie, and J. F. Hussain, “Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization. Springer-Verlag Berlin Heidelber”,2013, pp.237-251.
[7] N. Rasouli, M.R.Meybodi, and H.Morshedlou, “Virtual machine placement in cloud systems using Learning Automata ”, Fuzzy Systems (IFSC), 2013 13th Iranian Conference on. Qazvin, 2013, pp. 1-5.