Task Scheduling Algorithm Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in Cloud Computing

محورهای موضوعی : Cloud, Cluster, Grid and P2P Computing

Ghazaal Emadi ¹ , Amir Masoud Rahmani ² , Hamed Shahhoseini ³

1 - Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Computer Engineering Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Science and Research Branch, Islamic Azad University, Tehran, Iran.

تاریخ دریافت : 1395/12/13 تاریخ پذیرش : 1396/04/21 تاریخ انتشار : 1396/05/10

کلید واژه: Convariance Matrix Adaptation Evolution Strategy(CMA-ES), cloud computing, Task Scheduling, Virtual Machines(Vms),

چکیده مقاله :

The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including ever-increasing advancements of information technology and an increase of applications and user needs for these applications with high quality, as well as, the popularity of cloud computing among user and rapidly growth of them during recent years. This research presents the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an evolutionary algorithm in the field of optimization for tasks scheduling in the cloud computing environment. The findings indicate that presented algorithm, led to a reduction in execution time of all tasks, compared to SPT, LPT, and RLPT algorithms.Keywords: Cloud Computing, Task Scheduling, Virtual Machines (VMs), Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

منابع و مأخذ:

1. Choudhary M., Peddoju S.K.,( 2012). A Dynamic Optimization Algorithm for Task scheduling in Cloud Environment, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 3, pp. 2564–2568.
2. Loshchilov, Ilya, et al. "Maximum likelihood-based online adaptation of hyper-parameters in CMA-ES." International Conference on Parallel Problem Solving from Nature. Springer International Publishing, 2014.
3. Mell P., Grance T., (2013). "The NIST Definition of Cloud Computing":http://productionscale.com/blog/2011/8/7/the-nist-definition-of-cloud-computingdrft.html, [Accessed: 20-Dec-2013].
4. Jorge Peñarrubia1 , Facundo A. Gómez , Gurtina Besla , Denis Erkal4 & Yin-Zhe Ma., (2015)., “A timing constraint on the (total) mass of the Large Magellanic Cloud”., Astrophysics of Galaxies.
5. Agnetis.A, Billaut.Ch.J, Gawiejnowicz.S, Pacciarelli.D, Soukhal.A, (2014). Multiagent Scheduling, Models and Algorithms, Springer US .
6. Liu.J, (2013). Job Scheduling Model for Cloud Computing Based on Multi-Objective Genetic Algorithm, International Journal of Computer Science Issues ISSN : 1694-0814 .
7. Ghorbannia Delavar, A., Javanmard , M., Barzegar Shabestari and Marjan Khosravi Talebi ., (2012). “RSDC (RELIABLE SCHEDULING DISTRIBUTED IN CLOUD COMPUTING)” in International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.3, June 2012.
8. M. Dakshayini, Dr. H. S. Gurupras.ad. (2011). “An Optimal Model for Priority based Service Scheduling Policy for Cloud Computing Environment” International Journal of Computer Applications (0975 – 8887) Volume 32– No.9.
9. Shamsollah Ghanbari, Mohamed Othman. (2012). “A Priority based Job Scheduling Algorithm in Cloud Computing” International Conference on Advances Science and Contemporary Engineering.
10. Hansen, N. (2011). The CMA Evolution Strategy: A Tutorial. June 28.
11. Nagadevi.S, Satyapriya.K, Malathy.D. (2013). A Survey on Economic cloud schedulers for optimized task scheduling, International Journal of Advanced Engineering Technology, Vol 5, pp: 58-62.
12. Jafari Navimipour, Nima, Amir Masoud Rahmani, Ahmad Habibizad Navin, and Mehdi Hosseinzadeh. "Job scheduling in the Expert Cloud based on genetic algorithms." Kybernetes 43, no. 8 (2014): 1262-1275.
13. Rahmani, Amir Masoud, and Mojtaba Rezvani. "A novel genetic algorithm for static task scheduling in distributed systems." International Journal of Computer Theory and Engineering 1.1 (2009): 1.
14. Adabi, Sahar, Ali Movaghar, and Amir Masoud Rahmani. "Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources." The Journal of Supercomputing 67.1 (2014): 175-218.
15. Dashti, Seyed Ebrahim, and Amir Masoud Rahmani. "Dynamic VMs placement for energy efficiency by PSO in cloud computing." Journal of Experimental & Theoretical Artificial Intelligence 28.1-2 (2016): 97-112.
16. Kazem, Ali Asghar Pourhaji, Amir Masoud Rahmani, and Hamed Habibi Aghdam. "A modified simulated annealing algorithm for static task scheduling in grid computing." Computer Science and Information Technology, 2008. ICCSIT'08. International Conference on. IEEE, 2008.