Journal of Grid Computing

, Volume 14, Issue 2, pp 327–345 | Cite as

Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach

Article

Abstract

Energy efficiency has grown into a latest exploration area of virtualized cloud computing paradigm. The increase in the number and the size of the cloud data centers has propagated the need for energy efficiency. An extensively practiced technology in cloud computing is live virtual machine migration and is thus focused in this work to save energy. This paper proposes an energy-aware virtual machine migration technique for cloud computing, which is based on the Firefly algorithm. The proposed technique migrates the maximally loaded virtual machine to the least loaded active node while maintaining the performance and energy efficiency of the data centers. The efficacy of the proposed technique is exhibited by comparing it with other techniques using the CloudSim simulator. An enhancement in the average energy consumption of about 44.39 % has been attained by reducing an average of 72.34 % of migrations and saving 34.36 % of hosts, thereby, making the data center more energy-aware.

Keywords

Cloud computing Energy awareness Firefly optimization Virtualization Virtual Machine (VM) migration 

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References

  1. 1.
    Rings, T., Caryer, G., Gallop, J., Grabowski, J., Kovacikova, T., Schulz, S., Stokes-Rees, I.: Grid and cloud computing: opportunities for integration with the next generation network. J. Grid Comput. 7(3), 375–393 (2009)CrossRefGoogle Scholar
  2. 2.
    Min, C., Kim, I., Kim, T., Eom, Y.I.: VMMB: Virtual Machine Memory Balancing for Unmodified Operating Systems. J. Grid Comput. 10(1), 69–84 (2012)CrossRefGoogle Scholar
  3. 3.
    Rodero, I., Viswanathan, H., Lee, E.K., Gamell, M., Pompili, D., Parashar, M.: Energy-efficient thermal-aware autonomic management of virtualized HPC cloud infrastructure. J. Grid Comput. 10(3), 447–473 (2012)CrossRefGoogle Scholar
  4. 4.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK (2008)Google Scholar
  5. 5.
    Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Eugmann, T. (eds.) The proceedings of 5th Symposium on Stochastic Algorithms, Foundations and Applications. Lecture Notes in Computer Science, vol. 5792, pp 169–178. Springer, Berlin (2009)Google Scholar
  6. 6.
    Kansal, N.J., Chana, I.: Artificial bee colony based energy-aware resource utilization technique for cloud computing. Concurreny and Computation: Practice and Experience (CCPE), vol. 27, Issue 5. Wiley Online Library, pp. 1207–1225. doi: 10.1002/cpe.3295 (2014)
  7. 7.
    Yang, X.S., He, X. : Firefly algorithm: recent advances and applications. Int. J. Swarm Intell. 1 (1), 36–50 (2013). doi: 10.1504/IJSI.2013.055801 CrossRefGoogle Scholar
  8. 8.
    Khaze, S.R., Maleki, I., Hojjatkhah, S., Bagherinia, A.: Evaluation the efiiciency of artificial bee colony and the firefly algorithm in solving the continuous optimization problem. Int. J. Comput. Sci. Appl. (IJCSA) 3(4 ) (2013)Google Scholar
  9. 9.
    Basu, B., Mahanti, G.K.: Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna. Progr. Electromagn. Res. B 32, 169–190 (2011)CrossRefGoogle Scholar
  10. 10.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility, vol. 25, pp 599–616 (2009). 6Google Scholar
  11. 11.
    Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I., Warfield, A.: Live migration of virtual machines. In: Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation, pp. 273–286 (2005)Google Scholar
  12. 12.
    Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)CrossRefGoogle Scholar
  13. 13.
    Ghribi, C., Hadji, M., Zeghlache, D.: Energy efficient VM scheduling for cloud data centers: exact allocation and migration algorithms. In: Proceedings of 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (2013), doi: 10.1109/CCGrid.2013.89
  14. 14.
    Man, C.L.T, Kayashima, M.: Virtual machine placement algorithm for virtualized desktop infrastructure. In: Proceedings of IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), Beijing, pp. 333–337 (2011)Google Scholar
  15. 15.
    Voss, S.: Meta-heuristics: the state of the art. In: Nareyek, A. (ed.) Local Search for Planning and Scheduling. Lecture Notes in Artificial Intelligence, vol. 2148, pp 1–23. Springer, Berlin (2001)Google Scholar
  16. 16.
    Weiss, A.: Computing in the clouds. Networker Mag. 11(4), 16–25 (2007)CrossRefGoogle Scholar
  17. 17.
    Beloglazov, A., Buyya, R.: Energy efficient allocation of virtual machines in cloud data centers. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), Melbourne, Australia, pp. 577–578 (2010)Google Scholar
  18. 18.
    Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60(2), 268–280 (2012)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Dorigo, M., Colorni, A.: The ant system optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 26(1), 1–13 (1996)CrossRefGoogle Scholar
  20. 20.
    Dorigo, M., Gambardella, L.M.: Ant colonies for the traveling salesman problem. BioSystems 43, 73–81 (1997)CrossRefGoogle Scholar
  21. 21.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report - TR06, October (2005)Google Scholar
  22. 22.
    Ludwig, S.A., Moallem, A.: Swarm intelligence approaches for grid load balancing. J. Grid Comput. 9(3), 279–301 (2011)CrossRefGoogle Scholar
  23. 23.
    Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2, 353–373 (2005)CrossRefGoogle Scholar
  24. 24.
    Tarighi, M., Motamedi, S.A., Sharifian S.: A new model for virtual machine migration in virtualized cluster server based on fuzzy decision making. J. Telecommun. 1(1), 40–51 (2010)Google Scholar
  25. 25.
    Wood, T., Shenoy, P.J., Venkataramani, A., Yousif, M.S.: Black-box and gray-box strategies for virtual machine migration. In: Proceedings of 4th USENIX Symposium on Networked Systems Design and Implementation (NSDI’07), Cambridge, pp. 229–242 (2007)Google Scholar
  26. 26.
    Wood, T., Shenoy, P.J., Venkataramani, A., Yousif, M.S.: Sandpiper: black-box and gray-box resource management for virtual machines. Comput. Netw. 53(17), 2923–2938 (2009)CrossRefMATHGoogle Scholar
  27. 27.
    Lim, M.Y., Rawson, F., Bletsch, T., Freeh, V.W.: PADD: power aware domain distribution. In: Proceedings of the 29th IEEE International Conference on Distributed Computing Systems (ICDCS’09), Montreal, pp. 239–247 (2009)Google Scholar
  28. 28.
    Verma, A., Ahuja, P., Neogi, A.: pMapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware (Middleware’08), Leuven, Belgium, pp. 243–264. Springer, Berlin (2008)Google Scholar
  29. 29.
    Tolia, N., Wang, Z., Marwah, M., Bash, C., Ranganathan, P., Zhu, X.: Delivering energy proportionality with non energy-proportional systems - optimizing the ensemble. In: Workshop on Power Aware Computing and Systems (HotPower ’08), San Diego (2008)Google Scholar
  30. 30.
    Feller, E., Rilling, L., Morin, C.: Snooze: a scalable and autonomic virtual machine management framework for private clouds. Rapport de recherche RR-7833, INRIA (2011 )Google Scholar
  31. 31.
    Mastroianni, C., Meo, M., Papuzzo, G.: Self-economy in cloud data centers: statistical assignment and migration of virtual machines. Euro-Par 2011 Parallel Processing, pp. 407–418 (2011)Google Scholar
  32. 32.
    Marzolla, M., Babaoglu, O., Panzieri, F.: Server consolidation in clouds through gossiping. In: IEEE International Symposium on the World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–6. IEEE (2011)Google Scholar
  33. 33.
    Murtazaev, A., Oh, S.: Sercon: server consolidation algorithm using live migration of virtual machines for green computing. IETE Tech. Rev. 28(3), 212–231 (2011)CrossRefGoogle Scholar
  34. 34.
    Verma, A., Dasgupta, G., Nayak, T., De, P., Kothari, R.: Server Workload Analysis for Power Minimization Using Consolidation, p 28. USENIX Association , Berkeley (2009)Google Scholar
  35. 35.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing. J. Futur. Gener. Comput. Syst. 28(5), 755–768 (2012)CrossRefGoogle Scholar
  36. 36.
    Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: Proceedings of 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 826–831 (2010), doi: 10.1109/CCGRID.2010.46
  37. 37.
    Cardosa, M., Korupolu, M., Singh, A.: Shares and utilities based power consolidation in virtualized server environments. In: Proceedings of IEEE, pp. 327–334 (2009), doi: 10.1109/INM.2009.5188832
  38. 38.
    Goiri, I., Berral, J.L., Fitó, O., Julià, F., Nou, R., Guitart, J., Gavalda, R., Torres, J.: Energy-efficient and multifaceted resource management for profit-driven virtualized data centers. Futur. Gener. Comput. Syst. 28(5), 718–731 (2012)CrossRefGoogle Scholar
  39. 39.
    Graubner, P., Schmidt, M., Freisleben, B.: Energy-efficient management of virtual machines in eucalyptus. In: Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing, ser. CLOUD ’11, pp. 243–250. [Online]. Available. doi: 10.1109/CLOUD.2011.26 (2011)
  40. 40.
    Mehta, S., Neogi, A.: ReCon: a tool to recommend dynamic server consolidation in multi-cluster data centers. In: Proceedings of the IEEE Network Operations and Management Symposium, NOMS’08, Salvador (2008)Google Scholar
  41. 41.
    Dong, J., Jin, X., Wang, H., Li, Y., Zhang, P., Cheng, S.: Energy-saving virtual machine placement in cloud data centers. In: Proceedings of 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (2013)Google Scholar
  42. 42.
    Xiaoli, W., Zhanghui, L.: An energy-aware VMs placement algorithm in cloud computing environment. In: Proceedings of the Second International Conference on Intelligent System Design and Engineering Application. IEEE (2012)Google Scholar
  43. 43.
    Vu, H.T., Hwang, S.: A traffic and power-aware algorithm for virtual machine placement in cloud data center. Int. J. Grid Distrib. Comput. 7(1), 21 (2014)CrossRefGoogle Scholar
  44. 44.
    Beloglazov, A., Buyya R.: Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science, MGC ’2010, Bangalore (2010)Google Scholar
  45. 45.
    Nathuji, R., Schwan, K.: Virtualpower: coordinated power management in virtualized enterprise systems. ACM SIGOPS Oper. Syst. Rev. 41(6), 265–278 (2007)CrossRefGoogle Scholar
  46. 46.
    Sekhar, J., Jeba, G.: Energy efficient VM live migration in cloud data centers. Int. J. Comput. Sci. Netw. (IJCSN) 2(2), 71–75 (2013)Google Scholar
  47. 47.
    Jo, C., Gustafsson, E., Son, J., Egger, B.: Efficient live migration of virtual machines using shared storage. In: Proceedings of the 9th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE’13, Houston, pp. 41–50 (2013)Google Scholar
  48. 48.
    Strunk, A., Dargie, W.: Does live migration of virtual machines cost energy?. In: Proceedings of the 27th IEEE International Conference on Advanced Information Networking and Applications, pp. 514–521 (2013)Google Scholar
  49. 49.
    Bila, N., Lara, E.D., Joshi, K., Lagar-Cavilla, H.A., Hiltunen, M., Satyanarayanan, M.: Jettison: efficient idle desktop consolidation with partial vm migration. In: Proceedings of the 7th ACM European Conference on Computer Systems. EuroSys ’12, New York, pp. 211–224 (2012)Google Scholar
  50. 50.
    Jung, G., Hiltunen, M., Joshi, K., Schlichting, R., Pu, C.: Mistral: dynamically managing power, performance, and adaptation cost in cloud infrastructures. In: Proceedings of 30th IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 62 –73 (2010)Google Scholar
  51. 51.
    Setzer, T., Stage, A.: Decision support for virtual machine reassignments in enterprise data centers (2010)Google Scholar
  52. 52.
    Yue, M.: A simple proof of the inequality FFD(L) ≤ (11/9)OPT(L) + 1, for all L, for the FFD bin-packing algorithm. Acta Math. Appl. Sin. 7(4), 321–331 (1991)CrossRefMATHGoogle Scholar
  53. 53.
    Dressler, F., Akan, O.B.: A survey on bio-inspired networking. Comput. Netw. J. 54(6), 881–900 (2010)CrossRefMATHGoogle Scholar
  54. 54.
    Meisel, M., Pappas, V., Zhang, L.: A taxonomy of biologically inspired research in computer networking. Comput. Netw. J. 54(6), 901–916 (2010)CrossRefMATHGoogle Scholar
  55. 55.
    Vecchiola, C., Chu, X., Buyya, R.: Aneka: a software platform for.NET-based cloud computing. High Performance & Large Scale Comp. Advances in Parallel Computing 267–295 (2009)Google Scholar
  56. 56.
    Kaur, T., Chana, I.: Energy efficiency techniques in cloud computing - a survey and taxonomy. ACM Comput. Surv. 48(2, Article 22, 46 pp) (2015)Google Scholar
  57. 57.
    Beloglazov, A., Buyya, R., Lee, Y.C., Zomaya, A.: A taxonomy and survey of energy efficient data centers and cloud computing systems. Adv. Comput. 82(2), 47–111 (2011)CrossRefGoogle Scholar
  58. 58.
    Minas, L., Ellison, B.: Energy Efficiency for Information Technology: How to Reduce Power Consumption in Servers and Data Centers (2009)Google Scholar
  59. 59.
    Buyya, R., Ranjan, R., Calheiros R.N.: Modeling and simulation of scalable cloud computing environments and the cloudSim Toolkit: challenges and opportunities. In: Proceedings of the 7th High Performance Computing & Simulation Conference (HPCS 2009), pp. 1–11, Leipzig, Germany, pp. 21–24. IEEE Press, New York (2009)Google Scholar
  60. 60.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., Rose, C.A.F.D., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. In: Software: Practice and Experience (SPE), vol. 41, Issue 1, pp. 23–50, ISSN 0038–0644. Wiley Press, New York (2011)Google Scholar
  61. 61.
    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. Concurrency and Computation: Practice and Experience (CCPE), vol. 24, Issue 13, pp. 1397–1420. Wiley, New York (2012)Google Scholar
  62. 62.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control. Syst. Mag. 22, 52–67 (2002)CrossRefGoogle Scholar
  63. 63.
    Zhou, A., Wang, S., Zheng, Z., Hsu, C., Lyu, M., Yang, F.: On cloud service reliability enhancement with optimal resource usage. In: IEEE Transactions on Cloud Computing, vol. PP, no. 99, pp. 1–1 (2014). doi: 10.1109/TCC.2014.2369421

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Computer Science, Engineering DepartmentThapar UniversityPatialaIndia

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