Document Type: Original Research Paper

Authors

1 Department of Electronic and Computer Engineering, Institute for Higher Education Pouyandegan Danesh, Chalous, Iran

2 Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, IRAN

3 Department of Mathematics, Chalous Branch, Islamic Azad University, Chalous, IRAN

Abstract

Migration of Virtual Machine (VM) is a critical challenge in cloud computing. The process to move VMs or applications from one Physical Machine (PM) to another is known as VM migration. In VM migration several issues should be considered. One of the major issues in VM migration problem is selecting an appropriate PM as a destination for a migrating VM. To face this issue, several approaches are proposed that focus on ranking potential destination PMs by addressing migration objectives. In this paper we propose a new hierarchal fuzzy logic system for ranking potential destination PMs for a migrating VM by considering following parameters: Performance efficiency, Communication cost between VMs, Power consumption, Workload, Temperature efficiency and Availability. Using hierarchal fuzzy logic systems which consider the mentioned six parameters which have great role in ranking of potential destination PMs for a migrating VM together, the accuracy of PMs ranking approach is increased, furthermore the number of fuzzy rules in the system are reduced, thereby reducing the computational time (which is critical in cloud environment). In our experiments, we compare our proposed approach that is named as (HFLSRPM: Hierarchal Fuzzy Logic Structure for Ranking potential destination PMs for a migrating VM) with AppAware algorithm in terms of communication cost and performance efficiency. The results demonstrate that by considering more effective parameters in the proposed PMs ranking approach, HFLSRPM outperforms AppAware algorithm.

Keywords

Main Subjects

[1] R. Boutaba, Q. Zhang and M. F. Zhani, "Virtual Machine Migration in Cloud Computing Environments. Benefits, Challenges, and Approaches", IGI Global, 2013, pp. 383-408.
[2] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, and A. Warfield, "Live migration of virtual machines", In Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation, vol.2 , 2005, pp. 273-286.
[3] M. Mohammadian., "Designing Customized Hierarchical Fuzzy Logic Systems For Modelling and Prediction", 4thAsian-Pacific Conference on Simulated Evolution and Learning, pp.18-22, 2002, Singapore.
[4] G. V. S.Raju, J. Zhou., “Adaptive Hierarchical Fuzzy Controller”, IEEE Transactions on Systems, Man & Cybernetics, vol 23, no.4,1993. pp. 973-980.
[5] T. Wood, P. Shenoy, A. Venkataramani, and M. Yousif, "Sandpiper. Black-box and gray-box resource management for virtual machines", Computer Networks, vol.53, no.17, 2009, pp. 2923-2938.
[6] J.Xu, andJ. Fortes, "A multi-objective approach to virtual machine management in datacenters", In Proceedings of the 8th ACM international conference on Autonomic computing, 2010, pp. 225–234.
[7] M.Tarighi, S. A. Motamedi and S.Sharifian, "A new model for virtual machine migration in virtualized cluster server based on Fuzzy Decision Making", arXiv preprint arXiv.1002.33292010.
[8] G. Jung, M. A.Hiltunen, K. R. Joshi, R. D.Schlichting, and C.Pu, "Mistral. Dynamically managing power, performance, and adaptation cost in cloud infrastructures", In Proceedings of the IEEE International Conference on Distributed Computing Systems ICDCS, 2010, pp.62–73.
[9] H. Liu, H. Jin, C. Z.Xu, and X. Liao, "Performance and energy modeling for live migration of virtual machines", Cluster computing, vol.16, no.2, 2013, pp.249-264.
[10] V. Shrivastava, P.Zerfos, K.W.Lee, H.Jamjoom, Y.H. Liu, and S. Banerjee, "Application-aware virtual machine migration in data centers", In Proceedings of IEEE INFOCOM, 2011, pp. 66 -70.
[11] C.Thraves and L. Wang, “Power-Efficient Assignment of Virtual Machines to Physical Machines. In Adaptive Resource Management and Scheduling for Cloud Computing”, 2014, In first International Workshop, ARMS-CC 2014, held in Conjunction with ACM Symposium on Principles of Distributed Computing, PODC 2014, Paris, France, July 15, 2014, Revised Selected Papers, vol. 8907, pp. 71.
[12] F. Tao, C.Li,T. Liao and Y.Laili, “BGM-BLA: a new algorithm for dynamic migration of virtual machines in cloud computing”, 2015.
[13] Garg, S. K., Versteeg, S., and Buyya, R., "A framework for ranking of cloud computing services", Future Generation Computer Systems, vol.29, no.4, 2013, pp.1012-1023.
[14] A. Burkimsher, I. Bate and L. S. Indrusiak, "A survey of scheduling metrics and an improved ordering policy for list schedulers operanking on workloads with dependencies and a wide variation in execution times", Future Generation Computer Systems,vol. 29, no.8, 2013, pp. 2009-2025.
[15] A. Sallam, K. Li, A. Ouyang and Zh. Li, "Proactive workload management in dynamic virtualized environments", In Journal of Computer and system Science, 2014, vol.80, pp.1504-1517.
[16] T. J. Ross, “Fuzzy logic with engineering applications”. McGraw-Hill, New York, 1995.
[17] D. Minarolli, and B. Freisleben, “Distributed Resource Allocation to Virtual Machines via Artificial Neural Networks”, In Parallel, Distributed and Network-Based Processing PDP, 2014 22nd Euromicro International Conference on, 2014, pp. 490-499.
[18] J. Sonnek, J.Greensky, R.Reutiman, andA. Chandra, "Starling. Minimizing communication overhead in virtualized computing platforms using decentralized affinity-aware migration", In Parallel Processing ICPP, 2010 39th International Conference on, 2010, September, pp. 228-237.
[19] A. Hammadi, and L. Mhamdi, "A survey on architectures and energy efficiency in Data Center Networks", Computer Communications, vol. 40, 2014, pp.1-21.
[20] X. Meng, V. Pappas and L. Zhang, “Improving the scalability of data center networks with traffic-aware virtual machine placement”, In INFOCOM, 2010 Proceedings IEEE, 2010, pp. 1-9.