Abstract
Virtualization technology plays an important role in cloud computing. Virtual machine (VM) migration not only enables load balancing of hosts in data center to avoid overload anomalies, but also reduces the cost of cloud computing data centers. Our work mainly focused on the communication costs of VMs migration in data center. In this paper, a double auction-based VM migration algorithm is proposed, which takes the cost of communication between VMs into account under normal operation situation. The algorithm of VM migration is divided into two parts: (i) selecting the VMs to be migrated according to the communication and occupied resources factors of VMs and (ii) determining the destination host for VMs which to be migrated. In the first process of VM migration, we proposed VMs greedy selection algorithm (VMs-GSA) to select VMs. A VM Migration Double Auction Mechanism was applied to the second process of VM migration to obtain the mappings between VMs and underutilized hosts. The simulation result shows that the proposed VM migration algorithm-based heuristic is efficient. The traffic generated by VMs-GSA is 35% less than the random algorithm, and the success rate of VM migration is very high.
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References
Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2018) An improved lvy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust Comput 1:1–16
Azougaghe A, Oualhaj OA, Hedabou M (2017) Many-to-one matching game towards secure virtual machines migration in cloud computing. In: International conference on advanced communication systems and information security, pp 1–7
Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768
Chen J, Liu W, Song J (2012) Network performance-aware virtual machine migration in data centers. In: The third international conference on cloud computing, GRIDs, and virtualization Cloud Comput. Nice, France, pp 65–71
Gao C, Wang H, Zhai L, Gao Y, Yi S (2017) An energy-aware ant colony algorithm for network-aware virtual machine placement in cloud computing. In: IEEE international conference on parallel and distributed systems, pp 669–676
Goldberg RP (1974) Survey of virtual machine research. Computer 7(6):34–45
Heller B (2010) Saving energy in data center networks. Nsdi10 Apr
Huang J, Wu K, Moh M (2014) Dynamic virtual machine migration algorithms using enhanced energy consumption model for green cloud data centers. In: International conference on high performance computing and simulation, pp 902–910
Kansal NJ, Chana I (2016) Energy-aware virtual machine migration for cloud computing–a firefly optimization approach. J Grid Comput 14(2):327–345
Li Y, Lu H, Nakayama Y, Kim H, Serikawa S (2018) Automatic road detection system for an airland amphibious car drone. Future Gener Comput Syst 85:51–59
Lu H, Li Y, Mu S (2017a) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J PP(99):1–1
Lu H, Li B, Zhu J, Li Y (2017b) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comput Pract Exp 29(6):e3927. https://doi.org/10.1002/cpe.3927
Lu H, Li Y, Chen M, Kim H, Serikawa S (2018a) Brain intelligence: go beyond artificial intelligence. Mob Netw Appl 23(2):368–375
Lu H, Li Y, Uemura T, Kim H, Serikawa S (2018b) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Gener Comput Syst 82:142–148
Marahatta A, Wang Y, Zhang F, Sangaiah AK, Tyagi SKS, Liu Z (2018) Energy-aware fault-tolerant dynamic task scheduling scheme for virtualized cloud data centers. Mob Netw Appl. https://doi.org/10.1007/s11036-018-1062-7
Medhane DV, Sangaiah AK (2018) Pcca: Position confidentiality conserving algorithm for content-protection in e-governance services and applications. IEEE Trans Emerg Top Comput Intell 2(3):194–203
Meng X, Pappas V, Zhang L (2010) Improving the scalability of data center networks with traffic-aware virtual machine placement. In: Proceedings IEEE INFOCOM, San Diego, 14–19 Mar 2010. IEEE, pp 1–9. https://doi.org/10.1109/INFCOM.2010.5461930
Reguri VR, Kogatam S, Moh M (2016) Energy efficient traffic-aware virtual machine migration in green cloud data centers. In: IEEE international conference on big data security on cloud, pp 268–273
Rings T, Caryer G, Gallop JR, Grabowski J, Kovacikova T, Schulz S, Stokesrees I (2009) Grid and cloud computing: opportunities for integration with the next generation network. J Grid Comput 7(3):375–393
Shrivastava V, Zerfos P, Lee K, Jamjoom H, Liu Y, Banerjee S (2011) Application-aware virtual machine migration in data centers. In: Proceedings IEEE INFOCOM, Shanghai, 10–15 Apr 2011. IEEE, pp 66–70. https://doi.org/10.1109/INFCOM.2011.5935247
Sun Z, Zhu Z, Chen L, Xu H, Huang L (2015) A combinatorial double auction mechanism for cloud resource group-buying. In: Performance computing and communications conference, pp 1–8
Sun J, Zhu G, Sun G, Liao D, Li Y, Sangaiah AK, Ramachandran M, Chang V (2018) A reliability-aware approach for resource efficient virtual network function deployment. IEEE Access PP(99):1–1
Tao F, Li C, Liao TW (2016) Bgm-bla: a new algorithm for dynamic migration of virtual machines in cloud computing. IEEE Trans Serv Comput 9(6):910–925
Vu HT, Hwang S (2014) A traffic and power-aware algorithm for virtual machine placement in cloud data center. Int J Grid Distrib Comput 7(1):350–355
Wang L, Von Laszewski G, Younge A (2010) Cloud computing: a perspective study. New Gener Comput 28(2):137–146
Xu X, He L, Lu H, Gao L, Ji Y (2018) Deep adversarial metric learning for cross-modal retrieval. World Wide Web. https://doi.org/10.1007/s11280-018-0541-x
Zaman S, Grosu D (2010) Combinatorial auction-based allocation of virtual machine instances in clouds. In: IEEE second international conference on cloud computing technology and science, pp 127–134
Zhang SM, Sangaiah AK (2018) Reliable design for virtual network requests with location constraints in edge-of-things computing. Eurasip J Wirel Commun Netw 2018(1):65
Zhang W, Han S, He H, Chen H (2017) Network-aware virtual machine migration in an overcommitted cloud. Future Gener Comput Syst 76:428–442
Acknowledgements
This work was supported by the National Nature Science Foundation of China under Grant 61872313 and Grant 61472344, in part by the Innovation Foundation for graduate students of Jiangsu Province under Grant CXLX12 0916, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions under Grant 14KJB520041, in part by the Advanced Joint Research Project of Technology Department of Jiangsu Province under Grant BY2015061-06 and Grant BY2015061-08, and in part by the Yangzhou Science and Technology under Grant YZ2017288 and Grant YZ2016245 and Yangzhou University Jiangdu High-end Equipment Engineering Technology Research Institute Open Project under Grant YDJD201707.
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Communicated by A. K. Sangaiah, H. Pham, M.-Y. Chen, H. Lu, F. Mercaldo.
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Zhu, J., Wang, J., Zhang, Y. et al. Virtual machine migration method based on load cognition. Soft Comput 23, 9439–9448 (2019). https://doi.org/10.1007/s00500-018-3599-6
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DOI: https://doi.org/10.1007/s00500-018-3599-6