A New Approach for Optimal Placement of Virtual Machines in Cloud Datacenters Using Discrete Gravitational Search Algorithm and Chaotic Functions

Document Type : Persian Original Article

Authors

1 Department of Computer, Islamic Azad University, Urmia Branch, Urmia, Iran

2 Department of Mathematics, Islamic Azad University, Urmia Branch, Urmia, Iran

Abstract

Placement of virtual machines on physical machines in cloud computing infrastructure is an important issue. Our approach for placement of virtual machines includes a mapping process of this machines on physical machines in cloud datacenters. Optimal placement results in lower power consumption, optimal usage of resources, traffic reduce in datacenters, decrease in costs and also increase in functionality of datacenters in cloud datacenters. In this paper we propose a discrete gravitational search algorithm and chaotic function for placement of virtual machines on physical machines in cloud datacenters. Our primary goal for proposing the approach is minimizing resource wastage, power consumption and network links. At the end of this paper we also compare our results with some other metaheuristic algorithms. Our results show that this approach is more effective than previous algorithms. by optimal placement in cloud datacenter, we can get best performance of our devices. Also we can do it by chaotic functions.

Keywords


[1] A. Beloglazov, R. Buyya, “Energy efficient resource management in virtualized cloud data centers,” Proceedings of the 2010 IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010, pp. 826-831.
[2] G. Lee, "Cloud Networking: Understanding Cloud Based Data Center Networks,” Morgan Kaufmann, 2014.
[3] Q. Zhang, L. Cheng, R. Boutaba, “Cloud computing: state-of-the-art and research challenges,“  Journal of Internet Services and Applications,  Vol. 1, No. 1, pp.7–18, 2010.
[4] Y. Fang, D. Tang, J. Ge, “Energy-aware schedule strategy based on dynamic migration of virtual machines in cloud computing,” Journal of Computational Information Systems,  Vol. 10, No. 8, pp. 201-208, 2012.
[5]  M. Alicherry, T. Lakshman, “Optimizing data access latencies in cloud systems by intelligent virtual machine placement,” 2013 Proceedings IEEE INFOCOM, 2013, pp. 647–655.
[6] D. Kusic, J. Kephart, J. Hanson, N. Kandasamy, G. Jiang, “Power and performance management of virtualized computing environments via lookahead control,” Cluster Computing, Vol. 12, No. 1, pp. 1–15, 2009.
[7] S. Chaisiri, B. Lee, D. Niyato, “Optimal virtual machine placement across multiple cloud providers,” Proceedings of the IEEE Asia-Pacific Services Computing Conference, 2009, pp. 103–110.
[8] H. Mi, H. Wang, G. Yin, Y. Zhou, D. Shi, L. Yuan, “Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers,” Proceedings of the IEEE International Conference on Services Computing, 2010, pp. 514–521.
[9] Y. Gao,  H. Guan, Z. Qi, Y. Houb, L. Liu, “A multi objective ant colony system algorithm for virtual machine placement in cloud computing,” Journal of Computer and System Sciences, Vol. 7, No. 9, pp. 1230-1242, 2013.
[10] T. Yang, Y. Choon, A. Zomaya, “Energy-Efficient Data Center Networks Planning with Virtual Machine Placement and Traffic Configuration,” 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, 2014, pp. 284-291.
[11] W. Ding, C. Gu, F. Luo, Y. Chang, U. Rugwiro, X. Li, G. Wen, “DFA-VMP: An efficient and secure virtual machine placement strategy under cloud environment,” The Institute of Electronics, Information and Communication Engineers Vol. 101, No. 7, pp. 203–206, 2018.
[12] E. Dashti, A. Rahmani, “Dynamic VMs placement for energy efficiency by PSO in cloud computing,” Journal of Experimental & Theoretical Artificial Intelligence, Vol. 28, No. 1, pp. 97–112, 2016.
[13] D.  Holliday, R. Resnick, J. Walker, “Fundamentals of physics,” John Wiley and Sons, 1993.
[14] E. Rashedi, “Gravitational Search Algorithm,” M.Sc. Thesis, Shahid Bahonar University of Kerman, Kerman, Iran, 2007.
[15] J Dong, H Wang, Y Li and S Cheng “Virtual machine placement optimizing to improve network performance in cloud data centers,” The Journal of China Universities of Posts and Telecommunications, Vol. 21, No. 3, pp. 62-70, 2014.
[16] M. Cheng, D.Prayogo, “Symbiotic Organisms Search: A new metaheuristic optimization algorithm,” Computers & Structures, Vol. 139, No. 15, pp. 98-112, 2014.
 [17] M. Eusuff, K. Lansey, F. Pasha, “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization,” Civil Engineering and Engineering Mechanics, Vol. 38, No. 2, pp. 129-154, 2006.
[18] A. Marphatia, A. Nuhnot, T. sachdeva, E. Shukla, L. Kurup, “Optimization of FCFS Based Resource Provisioning Algorithm for Cloud Computing,” IOSR Journal of Computer Engineering (IOSR-JCE), Vol. 10, No. 5, pp. 01-05, 2013.
[19] B. Santosa, L. Safitri, “Biogeography-based Optimization (BBO) Algorithm for Single Machine Total Weighted Tardiness Problem (SMTWTP),” The International Journal of Advanced Manufacturing Technology, Vol. 58, No. 9, pp. 1115–1129, 2011.