Energy Efficient Cloud Computing Vm Placement Based On Genetic Algorithm

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-44 Number-1
Year of Publication : 2017
Authors : Pooja Daharwal, Dr. Varsha Sharma
DOI :  10.14445/22312803/IJCTT-V44P103


Pooja Daharwal, Dr. Varsha Sharma   "Energy Efficient Cloud Computing Vm Placement Based On Genetic Algorithm". International Journal of Computer Trends and Technology (IJCTT) V44(1):15-23, February 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
In the age of large data and the large number of users around the world, cloud computing has emerged as a new era of computing. Cloud consists of a datacenter which in turn consists of several physical machines. Each machine is shared by many users and virtual machines are used to use these physical machines. With a large number of datacenters and each datacenter having a large number of physical machines. The VM allocation becomes an NP-Hard problem. Thus, the VM allocation, the VM migration becomes a trivial task. In this article, a survey is carried out on cloud computing in energy cloud, based on scalable algorithms. To solve NP-Hard problems, there are two ways to either give an exact solution or to provide an approximation. The approximate solution is a time-efficient approach for solving NP-hard problems. In this research work, a survey on method for energy efficiency in cloud computing is carried out. The optimization of genetic algorithms has been studied in this research. And the genetic algorithm based VM placement algorithm is implemented for the energy efficiency of cloud operation.

[1] Meikang Qiu, Senior Member, “Phase- Change Memory Optimization for Green Cloud with Genetic Algorithm” In IEEE dec 2015.
[2] Rajwinder Kaur and Pawan Luthra “Load Balancing in Cloud Computing,” Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC 2014.
[3] Xiao-Fang Liu, Zhi-Hui Zhan, Jeremiah D. Deng, Yun Li, Tianlong Gu and Jun Zhang “An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing” In IEEE 2016.
[4] Dan Liu, Xin Sui, Li Li, “An Energy-efficient Virtual Machine Placement Algorithm in Cloud Data Center” 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) 2016.
[5]“Cloud Computing « Nexright Blog”.
[6] A. Sidhu, S. Kinger, “Analysis of load balancing techniques in cloud computing”, INTERNATIONAL JOURNALOF COMPUTERS & TECHNOLOGY 4 (2) (2013) pages737–741.
[7], “Software as a Service (SAAS) - Quality Testing”.
[8]“CloudComputing | Learn Cloud and its tips."
[9] Sahu, Yatendra and Pateriya, RK, “Cloud Computing Overview with Load Balancing Techniques”, International Journal of Computer Applications, 2013, vol. 65, Sahu2013
[10] Chaudhari, Anand and Kapadia, Anushka, “Load Balancing Algorithm for Azure Virtualization with Specialized VM”, 2013, algorithms,vol 1,pages 2, Chaudhari.
[11] Nayandeep Sran,Navdeep Kaur , “Comparative Analysis of Existing Load Balancing Techniques in Cloud Computing ”,vol 2,jan 2013
[12] Bala, Anju and Chana, Inderveer, “A survey of various workflow scheduling algorithms in cloud environment”, 2nd National Conference on Information and Communication Technology (NCICT), 2011.
[13] Chaczko, Zenon and Mahadevan, Venkatesh and Aslanzadeh, Shahrzad and Mcdermid, Christopher, “Availability and load balancing in cloud computing”, International Conference on Computer and Software Modeling, Singapore, chaczko2011availability.
[14] S.-C.Wang, K.-Q. Yan, S.-S.Wang, C.-W. Chen, “A three-phases scheduling in a hierarchical cloud computing network”, in: Communications and Mobile Computing (CMC), 2011 Third International Conference on,IEEE, 2011, pp. 114–117.
[15] O. Elzeki, M. Reshad, M. Elsoud, “Improved max-min algorithm in cloud computing, International Journal of Computer Applications”vol 50 (12) (2012) pages 22–27.
[16] U. Bhoi, P. N. Ramanuj, “ Enhanced max-min task scheduling algorithm in cloud computing”, International Journal of Application or Innovation in Engineering and Management (IJAIEM), ISSN,2013,pages 2319—4847.
[17] Y. Hu, R. Blake, D. Emerson, “An optimal migration algorithm for dynamic load balancing”, Concurrency: Practice and Experience 10 (6) (1998) pages 467–483.
[18] Nidhi Jain Kansal, Inderveer Chana, "Cloud LoadBalancing Techniques: A Step towards Green Computing", IJCSI, Vol. 9, Issue 1, January 2012.
[19] Nusrat Pasha, Dr. Amit Agarwal and Dr. Ravi Rastogi, “Round Robin Approach for VM Load Balancing Algorithm in Cloud Computing Environment” International Journal of Advanced Research in Computer Science and Software Engineering Volume 4, Issue 5, May 2014.
[20] Sreenivas Velagapudi, M.Prathap and Kemal Mohammed, “Load Balancing Techniques: Major Challenge in Cloud Computing – A Systematic Review,”IEEE, International Conference on Electronics and Communication Systems (ICECS) - Coimbatore, India (2014).
[21] S. Wang, K. Van, W. Liao, and S. Wang, "Towards a Load Balancing in a Three-level Cloud Computing Network", Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICC SIT), Chengdu, China, pp.108-113, September 2010.
[22] Shahrzad Aslanzadeh, Venkatesh Mahadevan, Christopher Mcdermid, “Availability and Load Balancing in Cloud Computing,” International Conference on Computer and Software Modeling IPCSIT vol.14 IACSIT Press, Singapore 2011.
[23] T. Kokilavani and Dr. D.I. George Amalarethinam, “Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing,” International Journal of Computer Applications Volume 20– No.2, pp.0975-8887, April 2011.

Data centre, Energy Consumption, Genetic Algorithm, Virtualization, Virtual Machine (VMs), VM Placement, Cloud Computing.