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An efficient energy-aware approach for dynamic VM consolidation on cloud platforms

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Abstract

The cloud computing environments rely heavily on virtualization that enables the physical hardware resources to be shared among cloud users by creating virtual machines (VMs). With an overloaded physical machine, the resource requests by virtual machines may not be fulfilled, which results in Service Level Agreement (SLA) violations. Moreover, the high performance servers in cloud data centers consume large amount of energy. The dynamic VM consolidation techniques use live migration of virtual machines to optimize resource utilization and minimize energy consumption. An excessive migration of virtual machines may however deteriorate application performance due to the overhead incurring at runtime. In this paper, we propose a normalization-based VM consolidation (NVMC) strategy that aims at placing virtual machines in an online manner while minimizing energy consumption, SLA violations, and the number of VM migrations. The proposed strategy uses resource parameters for determining over-utilized hosts in a virtualized cloud environment. The comparative capacity of virtual machines and hosts is incorporated for determining over-utilized hosts, while the cumulative available-to-total ratio (CATR) is used to find under-utilized hosts. For migrating virtual machines to appropriate hosts, the VM placement uses a criteria based on normalized resource parameters of hosts and virtual machines. For evaluating the performance of VM consolidation, we have performed experimentation with a large number of virtual machines using traces from the PlanetLab workloads. The results show that the NVMC approach outperforms other well-known approaches by achieving a significant improvement in energy consumption, SLA violations, and number of VM migrations.

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Data availability

The datasets used for experimentation in this research work are available with the well-known CloudSim simulator that may be accessed from the repository: https://github.com/Cloudslab/cloudsim/releases.

Notes

  1. With upper bound for competitive-ratio being \(1+(m*c/(2*(m+1)))\), where m is the maximum number of virtual machines that may be allocated to a host demanding maximum CPU capacity, and c is the cost of SLA violations.

References

  1. Al-Dulaimy, A., Itani, W., Zantout, R., Zekri, A.: Type-aware virtual machine management for energy efficient cloud data centers. Sustain. Comput. Inform. Syst. 19, 185–203 (2018) https://doi.org/10.1016/j.suscom.2018.05.012. http://www.sciencedirect.com/science/article/pii/S2210537917304249

  2. Azizi, S., Li, D., et al.: An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Clust. Comput. 23, 3421–3434 (2020). https://doi.org/10.1007/s10586-020-03096-0

  3. Belkhir, L., Elmeligi, A.: Assessing ict global emissions footprint: trends to 2040 & recommendations. J. Clean. Prod. 177, 448–463 (2018)

    Article  Google Scholar 

  4. Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24(7), 1366–1379 (2012)

    Article  Google Scholar 

  5. 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. Comput. Pract. Exp. 24(13), 1397–1420 (2012)

    Article  Google Scholar 

  6. Buyya, R., Broberg, J., Goscinski, A.M.: Cloud Computing Principles and Paradigms. Wiley Publishing, New York (2011)

    Book  Google Scholar 

  7. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011). https://doi.org/10.1002/spe.995

    Article  Google Scholar 

  8. Calheiros, R.N., Toosi, A.N., Vecchiola, C., Buyya, R.: A coordinator for scaling elastic applications across multiple clouds. Future Gener. Comput. Syst. 28(8), 1350–1362 (2012). https://doi.org/10.1016/j.future.2012.03.010. http://www.sciencedirect.com/science/article/pii/S0167739X12000635. Including Special sections SS: Trusting Software Behavior and SS: Economics of Computing Services

  9. Chen, M., Zhang, H., Su, Y., Wang, X., Jiang, G., Yoshihira, K.: Effective vm sizing in virtualized data centers. In: 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops, pp. 594–601 (2011). https://doi.org/10.1109/INM.2011.5990564

  10. Chun, B., Culler, D., Roscoe, T., Bavier, A., Peterson, L., Wawrzoniak, M., Bowman, M.: Planetlab: an overlay testbed for broad-coverage services. SIGCOMM Comput. Commun. Rev. 33(3), 3–12 (2003). https://doi.org/10.1145/956993.956995

    Article  Google Scholar 

  11. Ding, W., Luo, F., Han, L., Gu, C., Lu, H., Fuentes, J.: Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers. Future Gener. Comput. Syst. 111, 254–270 (2020). https://doi.org/10.1016/j.future.2020.05.004. http://www.sciencedirect.com/science/article/pii/S0167739X19307769

  12. Dong, J., Wang, H., Cheng, S.: Energy-performance tradeoffs in iaas cloud with virtual machine scheduling. China Commun. 12(2), 155–166 (2015). https://doi.org/10.1109/CC.2015.7084410

    Article  Google Scholar 

  13. Dupont, C., Schulze, T., Giuliani, G., Somov, A., Hermenier, F.: An energy aware framework for virtual machine placement in cloud federated data centres. In: 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy), pp. 1–10 (2012). https://doi.org/10.1145/2208828.2208832

  14. Duy, T.V.T., Sato, Y., Inoguchi, Y.: Performance evaluation of a green scheduling algorithm for energy savings in cloud computing. In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), pp. 1–8. IEEE (2010)

  15. Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. ACM SIGARCH Comput. Archit. News 35(2), 13–23 (2007)

    Article  Google Scholar 

  16. Farahnakian, F., Ashraf, A., Pahikkala, T., Liljeberg, P., Plosila, J., Porres, I., Tenhunen, H.: Using ant colony system to consolidate vms for green cloud computing. IEEE Trans. Serv. Comput. 8(2), 187–198 (2015). https://doi.org/10.1109/TSC.2014.2382555

    Article  Google Scholar 

  17. Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: Virtual machine consolidation in cloud data centers using aco metaheuristic. In: Silva, F., Dutra, I., Santos Costa, V. (eds.) Euro-Par 2014 Parallel Processing, pp. 306–317. Springer International Publishing, Cham (2014)

    Chapter  Google Scholar 

  18. Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)

    Article  MathSciNet  Google Scholar 

  19. Gharehpasha, S., Masdari, M., Jafarian, A.: Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm. Clust. Comput. 24(2), 1293–1315 (2021)

    Article  Google Scholar 

  20. Ghobaei-Arani, M., Rahmanian, A.A., Shamsi, M., Rasouli-Kenari, A.: A learning-based approach for virtual machine placement in cloud data centers. Int. J. Commun. Syst. 31(8), e3537 (2018). https://doi.org/10.1002/dac.3537. https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.3537. E3537 IJCS-17-0421.R1

  21. Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016)

    Article  MathSciNet  Google Scholar 

  22. Herbst, N.R., Kounev, S., Reussner, R.: Elasticity in cloud computing: what it is, and what it is not. In: Proceedings of the 10th International Conference on Autonomic Computing ({ICAC} 13), pp. 23–27 (2013)

  23. Hsieh, S.Y., Liu, C.S., Buyya, R., Zomaya, A.Y.: Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers. J. Parallel Distrib. Comput. 139, 99–109 (2020). https://doi.org/10.1016/j.jpdc.2019.12.014. http://www.sciencedirect.com/science/article/pii/S074373151930190X

  24. Laganà, D., Mastroianni, C., Meo, M., Renga, D.: Reducing the operational cost of cloud data centers through renewable energy. Algorithms 11(10), 145 (2018)

    Article  Google Scholar 

  25. Li, X., Qian, Z., Lu, S., Wu, J.: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Math. Comput. Model. 58(5), 1222–1235 (2013). https://doi.org/10.1016/j.mcm.2013.02.003. http://www.sciencedirect.com/science/article/pii/S0895717713000319. The Measurement of Undesirable Outputs: Models Development and Empirical Analyses and Advances in mobile, ubiquitous and cognitive computing

  26. Li, Z., Yan, C., Yu, L., Yu, X.: Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Future Gener. Comput. Syst. 80, 139–156 (2018) https://doi.org/10.1016/j.future.2017.09.075. http://www.sciencedirect.com/science/article/pii/S0167739X16307476

  27. Li, Z., Yu, X., Yu, L., Guo, S., Chang, V.: Energy-efficient and quality-aware vm consolidation method. Future Generat. Comput. Syst. 102, 789–809 (2020)

    Article  Google Scholar 

  28. Lin, C., Liu, P., Wu, J.: Energy-efficient virtual machine provision algorithms for cloud systems. In: 2011 Fourth IEEE International Conference on Utility and Cloud Computing, pp. 81–88 (2011). https://doi.org/10.1109/UCC.2011.21

  29. Liu, X.F., Zhan, Z.H., Deng, J.D., Li, Y., Gu, T., Zhang, J.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evol. Comput. 22(1), 113–128 (2016)

    Article  Google Scholar 

  30. Mastelic, T., Oleksiak, A., Claussen, H., Brandic, I., Pierson, J.M., Vasilakos, A.V.: Cloud computing: survey on energy efficiency. ACM Comput. Surv. 47(2), 1–36 (2014). https://doi.org/10.1145/2656204

    Article  Google Scholar 

  31. Mastroianni, C., Meo, M., Papuzzo, G.: Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans. Cloud Comput. 1(2), 215–228 (2013). https://doi.org/10.1109/TCC.2013.17

    Article  Google Scholar 

  32. Mi, H., Wang, H., Yin, G., Zhou, Y., Shi, D., Yuan, L.: Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: 2010 IEEE International Conference on Services Computing, pp. 514–521 (2010). https://doi.org/10.1109/SCC.2010.69

  33. Mosa, A., Paton, N.W.: Optimizing virtual machine placement for energy and sla in clouds using utility functions. J. Cloud Comput. 5(1), 17 (2016)

    Article  Google Scholar 

  34. Mytton, D.: How much energy do data centers use? (2020). https://davidmytton.blog/how-much-energy-do-data-centers-use/

  35. Park, K., Pai, V.S.: Comon: a mostly-scalable monitoring system for planetlab. SIGOPS Oper. Syst. Rev. 40(1), 65–74 (2006). https://doi.org/10.1145/1113361.1113374

    Article  Google Scholar 

  36. Salimian, L., Safi, F.: Survey of energy efficient data centers in cloud computing. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, UCC’13, pp. 369–374. IEEE Computer Society, USA (2013)

  37. Shabeera, T., Madhu Kumar, S., Salam, S.M., Murali Krishnan, K.: Optimizing vm allocation and data placement for data-intensive applications in cloud using aco metaheuristic algorithm. Eng. Sci. Technol. Int. J. 20(2), 616–628 (2017). https://doi.org/10.1016/j.jestch.2016.11.006. http://www.sciencedirect.com/science/article/pii/S2215098616304232

  38. Shehabi, A., Smith, S., Sartor, D., Brown, R., Herrlin, M., Koomey, J., Masanet, E., Horner, N., Azevedo, I., Lintner, W.: United states data center energy usage report. Tech. rep., Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States) (2016)

  39. Song, W., Xiao, Z., Chen, Q., Luo, H.: Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans. Comput. 63(11), 2647–2660 (2014). https://doi.org/10.1109/TC.2013.148

    Article  MathSciNet  MATH  Google Scholar 

  40. Speitkamp, B., Bichler, M.: A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans. Serv. Comput. 3(4), 266–278 (2010). https://doi.org/10.1109/TSC.2010.25

    Article  Google Scholar 

  41. Tarahomi, M., Izadi, M., Ghobaei-Arani, M.: An efficient power-aware VM allocation mechanism in cloud data centers: a micro genetic-based approach. Clust. Comput. 24, 919–934 (2021). https://doi.org/10.1007/s10586-020-03152-9

  42. Torre, E., Durillo, J.J., de Maio, V., Agrawal, P., Benedict, S., Saurabh, N., Prodan, R.: A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers. Inf. Softw. Technol. 128, 106390 (2020). https://doi.org/10.1016/j.infsof.2020.106390. http://www.sciencedirect.com/science/article/pii/S0950584919302101

  43. Wu, C.M., Chang, R.S., Chan, H.Y.: A green energy-efficient scheduling algorithm using the dvfs technique for cloud datacenters. Future Gener. Comput. Syst. 37, 141–147 (2014). https://doi.org/10.1016/j.future.2013.06.009. Special Section: Innovative Methods and Algorithms for Advanced Data-Intensive Computing Special Section: Semantics, Intelligent processing and services for big data Special Section: Advances in Data-Intensive Modelling and Simulation Special Section: Hybrid Intelligence for Growing Internet and its Applications

  44. Wu, Q., Ishikawa, F., Zhu, Q., Xia, Y.: Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters. IEEE Trans. Serv. Comput. 12(4), 550–563 (2019). https://doi.org/10.1109/TSC.2016.2616868

    Article  Google Scholar 

  45. Yadav, R., Zhang, W., Kaiwartya, O., Singh, P.R., Elgendy, I.A., Tian, Y.C.: Adaptive energy-aware algorithms for minimizing energy consumption and sla violation in cloud computing. IEEE Access 6, 55923–55936 (2018)

    Article  Google Scholar 

  46. Ye, X., Yin, Y., Lan, L.: Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment. IEEE Access 5, 16006–16020 (2017). https://doi.org/10.1109/ACCESS.2017.2733723

    Article  Google Scholar 

  47. Zhang, L., Zhuang, Y., Zhu, W.: Constraint programming based virtual cloud resources allocation model. Int. J. Hybrid Inf. Technol. 6(6), 333–344 (2013)

    Google Scholar 

  48. Zhang, Y., Ansari, N.: Heterogeneity aware dominant resource assistant heuristics for virtual machine consolidation. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 1297–1302 (2013). https://doi.org/10.1109/GLOCOM.2013.6831253

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Khan, M.A. An efficient energy-aware approach for dynamic VM consolidation on cloud platforms. Cluster Comput 24, 3293–3310 (2021). https://doi.org/10.1007/s10586-021-03341-0

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