Virtualization provides the flexibility to distribute the workload among physical servers to reduce overall electrical power consumption, through the consolidation of Virtual Machines (VMs). Many research projects have been done on VM migration to reduce energy consumption in data centers while ensuring a high level of adherence to the Service Level Agreements (SLA). Service levels of running applications are likely to be negatively affected during a live VM migration. For this reason, in this paper, we propose a new intelligent VM migration approach, called CLANFIC, which utilizes modified Cellular Learning Automata based Evolutionary Computing (CLA-EC) and neuro-fuzzy to minimize the number of VM migrations and improve energy consumption. This goal is achieved by utilizing an optimized placement method and delaying migration time based on future resource demand prediction. This algorithm reduces the number of migrations in two steps (i) finding the optimal virtual machine placement and replacement on physical servers by using modified CLA-EC (ii) predicting future resource usage in each host by a neuro-fuzzy algorithm to prevent unnecessary migrations. The experimental results on the real workload traces from PlanetLab show that the proposed method reduces the mean migration number, energy consumption, and SLA violation of the data center by 59.05%, 8.5%, and 70.76%, respectively.
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Gou, Z., Yamaguchi, S., Gupta, B.B.: Analysis of various security issues and challenges in cloud computing environment: a survey. In: Identity Theft: Breakthroughs in Research and Practice, pp. 221–247. IGI Global (2017)
Stergiou, C., Psannis, K.E., Kim, B.G., Gupta, B.: Secure integration of IoT and cloud computing. Future Gener. Comput. Syst. 78, 964–975 (2018)
Stergiou, C., Psannis, K.E., Gupta, B.B., Ishibashi, Y.: Security, privacy & efficiency of sustainable cloud computing for big data & IoT. Sustain. Comput. Inform. Syst. 19, 174–184 (2018)
Gupta, B.B. (ed.): Computer and Cyber Security: Principles, Algorithm, Applications, and Perspectives. CRC Press, Boca Raton (2018)
Gupta, B.B., Gupta, S., Chaudhary, P.: Enhancing the browser-side context-aware sanitization of suspicious HTML5 code for halting the DOM-based XSS vulnerabilities in cloud. Int. J. Cloud Appl. Comput. (IJCAC) 7(1), 1–31 (2017)
Hosseini, M., Salehi, M.A., Gottumukkala, R.: Enabling interactive video streaming for public safety monitoring through batch scheduling. In: 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 474–481. IEEE (2017)
Rastegar, R., Meybodi, M.R.: A new evolutionary computing model based on cellular learning automata. In: 2004 IEEE Conference on Cybernetics and intelligent systems, Vol. 1, pp. 433–438, IEEE (2004)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985)
Baker, B.S.: A new proof for the first-fit decreasing bin-packing algorithm. J. Algorithms 6(1), 49–70 (1985)
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)
Xue, W., Li, W., Qi, H., Li, K., Tao, X., Ji, X.: Communication-aware virtual machine migration in cloud data centres. Int. J. High Perform. Comput. Netw. 10(4–5), 372–380 (2017)
Dow, E.M., Fitzsimmons, T.D., Yu, J.: U.S. Patent No. 9,753,757. Washington, DC: U.S. Patent and Trademark Office (2017)
Huang, W., Gao, Q., Liu, J., Panda, D.K.: High performance virtual machine migration with RDMA over modern interconnects. In: 2007 IEEE International Conference on Cluster Computing, pp. 11–20, IEEE (2007)
Hu, B., Lei, Z., Lei, Y., Xu, D., Li, J.: A time-series based precopy approach for live migration of virtual machines. In: IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS), pp. 947–952, IEEE (2011)
Wu, Y., Zhao, M.: Performance modeling of virtual machine live migration. In: 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 492–499, IEEE (2011)
Jing, Y.: Key technologies and optimization for dynamic migration of virtual machines in cloud computing. In: 2012 Second International Conference on Intelligent System Design and Engineering Application (ISDEA), pp. 643–647, IEEE (2012)
Hines, M.R., Deshpande, U., Gopalan, K.: Post-copy live migration of virtual machines. ACM SIGOPS Oper. Syst. Rev. 43(3), 14–26 (2009)
Baruchi, A., Toshimi Midorikawa, E., Netto, M.: Improving Virtual Machine live migration via application-level workload analysis. In: 2014 10th International Conference on Network and Service Management (CNSM), pp. 163–168, IEEE (2014)
Agrawal, N., Pateriya, R.K.: Enhanced time series based pre-copy method for live migration of virtual machine. Int. J. Adv. Eng. Technol. 6(3), 1365–1372 (2013)
Hu, L., Zhao, J., Xu, G., Ding, Y., Chu, J.: HMDC: live virtual machine migration based on hybrid memory copy and delta compression. Appl. Math. 7(2L), 639–646 (2013)
Sagana, C., Geetha, M., Suganthe, R.C.: Performance enhancement in live migration for cloud computing environments. In: 2013 International Conference on Information Communication and Embedded Systems (ICICES), pp. 361–366, IEEE (2013)
Gustafsson, E.: Optimizing Total Migration Time in Virtual Machine Live Migration (2013)
Ye, K., Jiang, X., Huang, D., Chen, J., Wang, B.: Live migration of multiple virtual machines with resource reservation in cloud computing environments”, In: 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 267–274, IEEE (2011)
Salfner, F., Troeger, P., Richly, M.: Dependable estimation of downtime for virtual machine live migration. Int. J. Adv. Syst. Meas. 5(1) (2012)
Ferreto, T.C., Netto, M.A., Calheiros, R.N., De Rose, C.A.: Server consolidation with migration control for virtualized data centers. Future Gener. Comput. Syst. 27(8), 1027–1034 (2011)
Metkar, G., Agrawal, S., Singh, S.: A live migration of virtual machine based on the dynamic threshold at cloud data centres. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(10), 401–405 (2013)
Maurya, K., Sinha, R.: Energy conscious dynamic provisioning of virtual machines using adaptive migration thresholds in cloud data center. Int. J. Adv. Res. Comput. Sci. Mobile. Comput. 2, 74–82 (2013)
Zhang, X., Shae, Z.Y., Zheng, S., Jamjoom, H.: Virtual machine migration in an over-committed cloud. In: Network Operations and Management Symposium (NOMS), 2012 IEEE, pp. 196–203, IEEE (2012)
Daniel, S., Kwon, M.: Prediction-based virtual instance migration for balanced workload in the cloud datacenters (2011)
Gupta, Sh, Tiwari, D., Singh, Sh: Energy efficient dynamic threshold based load balancing technique in cloud computing environment. Int. J. Comput. Sci. Inf. Technol. 6(2), 1023–1026 (2015)
Chen, X., Chen, S., Tseng, F.H., Chou, L.D., Chao, H.C.: Minimizing virtual machine migration probability for cloud environments. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC), pp. 1430–1436, IEEE (2013)
Ts’epoMofolo, R.S.: Heuristic based resource allocation using virtual machine migration: a cloud computing perspective. Int. Refereed J. Eng. Sci. (IRJES) 2(5), 40–45 (2013)
Ghavipour, M., Meybodi, M.R.: An adaptive fuzzy recommender system based on learning automata. Electron. Commer. Res. Appl. 20, 105–115 (2016)
Ghavipour, M., Meybodi, M.R.: Irregular cellular learning automata-based algorithm for sampling social networks. Eng. Appl. Artif. Intell. 59, 244–259 (2017)
Jang, J.S.R., Mizutani, E.: Levenberg–Marquardt method for ANFIS learning. In: Fuzzy Information Processing Society, 1996. NAFIPS, 1996 Biennial Conference of the North American, pp. 87–91, IEEE (1996)
Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: Proceedings of the 19th ACM Symposium on Operating Systems Principles, SOSP 2003, Bolton Landing, NY, USA, p. 177 (2003)
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Computat. Pract. Exp. 24(13), 1397–1420 (2012)
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Jalali Moghaddam, M., Esmaeilzadeh, A., Ghavipour, M. et al. Minimizing virtual machine migration probability in cloud computing environments. Cluster Comput 23, 3029–3038 (2020). https://doi.org/10.1007/s10586-020-03067-5
- Virtual machine migration
- Service level agreement
- Virtual machine placement