Skip to main content
Log in

Green Cloud Computing Using Proactive Virtual Machine Placement: Challenges and Issues

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

Efficient VM management is very crucial for energy saving, increasing profit, and preventing SLA violations. VM placement schemes can be classified into reactive and proactive/predictive schemes which try to improve the VM placement results, by forecasting future workloads or resource demands using various prediction techniques. This paper puts forward an extensive survey of the proactive VM placement approaches and categorizes them according to their applied forecasting methods. It describes how each scheme has applied the prediction algorithms to conduct more effective and low overhead VM placement. Moreover, in each category, factors such as evaluation parameters, simulation software, workload data, power management method, and prediction factors are compared to illuminate more details about the investigated VM placement approaches. At last, the concluding issues and open future studies trends and area are highlighted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Masdari, M., Salehi, F., Jalali, M., Bidaki, M.: A survey of PSO-based scheduling algorithms in cloud computing. J. Netw. Syst. Manag. 25, 122–158 (2017)

    Google Scholar 

  2. Alizadeh, M., Abolfazli, S., Zamani, M., Baharun, S., Sakurai, K.: Authentication in mobile cloud computing: a survey. J. Netw. Comput. Appl. 61, 59–80 (2016)

    Google Scholar 

  3. Masdari, M., ValiKardan, S., Shahi, Z., Azar, S.I.: Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64–82 (2016)

    Google Scholar 

  4. Cheraghlou, M.N., Khadem-Zadeh, A., Haghparast, M.: A survey of fault tolerance architecture in cloud computing. J. Netw. Comput. Appl. 61, 81–92 (2016)

    Google Scholar 

  5. Masdari, M., Nabavi, S.S., Ahmadi, V.: An overview of virtual machine placement schemes in cloud computing. J. Netw. Comput. Appl. 66, 106–127 (2016)

    Google Scholar 

  6. Masdari, M., Jalali, M.: A survey and taxonomy of DoS attacks in cloud computing. Security and Communication Networks. 9, 3724–3751 (2016)

    Google Scholar 

  7. Ahmad, R.W., Gani, A., Hamid, S.H.A., Shiraz, M., Yousafzai, A., Xia, F.: A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J. Netw. Comput. Appl. 52, 11–25 (2015)

    Google Scholar 

  8. Song, F., Huang, D., Zhou, H., Zhang, H., You, I.: An optimization-based scheme for efficient virtual machine placement. Int. J. Parallel Prog. 42, 853–872 (2014)

    Google Scholar 

  9. Rong, H., Zhang, H., Xiao, S., Li, C., Hu, C.: Optimizing energy consumption for data centers. Renew. Sust. Energ. Rev. 58, 674–691 (2016)

    Google Scholar 

  10. J. Xu and J. Fortes, "A multi-objective approach to virtual machine management in datacenters," in Proceedings of the 8th ACM international conference on Autonomic computing, 2011, pp. 225–234

  11. Ding, Y., Qin, X., Liu, L., Wang, T.: Energy efficient scheduling of virtual machines in cloud with deadline constraint. Futur. Gener. Comput. Syst. 50, 62–74 (2015)

    Google Scholar 

  12. S. Chaisiri, B.-S. Lee, and D. Niyato, "Optimal virtual machine placement across multiple cloud providers," in Services Computing Conference, 2009. APSCC 2009. IEEE Asia-Pacific, 2009, pp. 103–110

  13. Weingärtner, R., Bräscher, G.B., Westphall, C.B.: Cloud resource management: a survey on forecasting and profiling models. J. Netw. Comput. Appl. 47, 99–106 (2015)

    Google Scholar 

  14. Roh, H., Jung, C., Kim, K., Pack, S., Lee, W.: Joint flow and virtual machine placement in hybrid cloud data centers. J. Netw. Comput. Appl. 85, 4–13 (2017)

    Google Scholar 

  15. Lin, W., Xu, S., Li, J., Xu, L., Peng, Z.: Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics. Soft. Comput. 21, 1301–1314 (2017)

    Google Scholar 

  16. Addya, S.K., Turuk, A.K., Sahoo, B., Satpathy, A., Sarkar, M.: A game theoretic approach to estimate fair cost of VM placement in cloud data center. IEEE Syst. J. 1–10 (2017)

  17. A.-p. Xiong and C.-x. Xu, "Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center," Mathematical Problems in Engineering, vol. 2014, 2014

  18. J. L. L. Simarro, R. Moreno-Vozmediano, R. S. Montero, and I. M. Llorente, "Dynamic placement of virtual machines for cost optimization in multi-cloud environments," in High Performance Computing and Simulation (HPCS), 2011 International Conference on, 2011, pp. 1–7

  19. M. Gahlawat and P. Sharma, "Survey of virtual machine placement in federated clouds," in 2014 IEEE International Advance Computing Conference (IACC), 2014, pp. 735–738

  20. Xu, M., Tian, W., Buyya, R.: A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurrency and Computation: Practice and Experience. 29, e4123 (2017)

    Google Scholar 

  21. Silva Filho, M.C., Monteiro, C.C., Inácio, P.R., Freire, M.M.: Approaches for optimizing virtual machine placement and migration in cloud environments: a survey. J Parallel Distributed Computing. 111, 222–250 (2018)

    Google Scholar 

  22. I. Pietri and R. Sakellariou, "Mapping virtual machines onto physical machines in cloud computing: A survey," ACM Computing Surveys (CSUR), vol. 49, p. 49, 2016

  23. Piper, A.: How to write a systematic literature review: a guide for medical students. National AMR, Fostering Medical Research. 1–8 (2013)

  24. De Maio, V., Prodan, R., Benedict, S., Kecskemeti, G.: Modelling energy consumption of network transfers and virtual machine migration. Futur. Gener. Comput. Syst. 56, 388–406 (2016)

    Google Scholar 

  25. Wang, B., Qi, Z., Ma, R., Guan, H., Vasilakos, A.V.: A survey on data center networking for cloud computing. Comput. Netw. 91, 528–547 (2015)

    Google Scholar 

  26. Qi, H., Shiraz, M., Liu, J.-y., Gani, A., Rahman, Z.A., Altameem, T.A.: Data center network architecture in cloud computing: review, taxonomy, and open research issues. J. Zhejiang University Sci. C. 15, 776–793 (2014)

    Google Scholar 

  27. C.-T. Yang, J.-C. Liu, K.-L. Huang, and F.-C. Jiang, "A method for managing green power of a virtual machine cluster in cloud," Future Generation Computer Systems, vol. 37, pp. 26–36, 2014/07/01/ 2014

  28. L. YamunaDevi, P. Aruna, D. S. Devi, and N. Priya, "Security in Virtual Machine Live Migration for KVM," in: International conference on process automation. Control. Comput. 2011, 1–6 (2011)

    Google Scholar 

  29. Zhang, W., Han, S., He, H., Chen, H.: Network-aware virtual machine migration in an overcommitted cloud. Futur. Gener. Comput. Syst. 76, 428–442 (2017)

    Google Scholar 

  30. Noshy, M., Ibrahim, A., Ali, H.A.: Optimization of live virtual machine migration in cloud computing: a survey and future directions. J. Netw. Comput. Appl. 110, 1–10 (2018)

    Google Scholar 

  31. Zhang, F., Liu, G., Fu, X., Yahyapour, R.: A survey on virtual machine migration: challenges, techniques, and open issues. IEEE Communications Surveys & Tutorials. 20, 1206–1243 (2018)

    Google Scholar 

  32. Ahmad, R.W., Gani, A., Hamid, S.H.A., Shiraz, M., Xia, F., Madani, S.A.: Virtual machine migration in cloud data centers: a review, taxonomy, and open research issues. J. Supercomput. 71, 2473–2515 (2015)

    Google Scholar 

  33. Khosravi, A., Nadjaran Toosi, A., Buyya, R.: Online virtual machine migration for renewable energy usage maximization in geographically distributed cloud data centers. Concurrency and Computation: Practice and Experience. 29, e4125 (2017)

    Google Scholar 

  34. P. Svärd, B. Hudzia, S. Walsh, J. Tordsson, and E. Elmroth, "The Noble Art of Live VM Migration-Principles and performance of pre copy, post copy and hybrid migration of demanding workloads," Technical report, 2014. Tech Report UMINF 14.10. Submitted, 2014

  35. Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Computing. 14, 217–264 (2016)

    Google Scholar 

  36. Arianyan, E., Taheri, H., Khoshdel, V.: Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. J. Netw. Comput. Appl. 78, 43–61 (2017)

    Google Scholar 

  37. Rossi, F.D., Xavier, M.G., De Rose, C.A., Calheiros, R.N., Buyya, R.: E-eco: performance-aware energy-efficient cloud data center orchestration. J. Netw. Comput. Appl. 78, 83–96 (2017)

    Google Scholar 

  38. Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Computing. 14, 55–74 (2016)

    Google Scholar 

  39. Stavrinides, G.L., Karatza, H.D.: An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Futur. Gener. Comput. Syst. 96, 216–226 (2019)

    Google Scholar 

  40. E. K. Lee, H. Viswanathan, and D. Pompili, "Vmap: Proactive thermal-aware virtual machine allocation in hpc cloud datacenters," in High Performance Computing (HiPC), 2012 19th International Conference on, 2012, pp. 1–10

  41. K. Le, R. Bianchini, J. Zhang, Y. Jaluria, J. Meng, and T. D. Nguyen, "Reducing electricity cost through virtual machine placement in high performance computing clouds," in Proceedings of 2011 International conference for high performance computing, Networking, Storage and Analysis, 2011, p. 22

  42. J. Kim, M. Ruggiero, D. Atienza, and M. Lederberger, "Correlation-aware virtual machine allocation for energy-efficient datacenters," in Proceedings of the Conference on Design, Automation and Test in Europe, 2013, pp. 1345–1350

  43. M. B. Nagpure, P. Dahiwale, and P. Marbate, "An efficient dynamic resource allocation strategy for VM environment in cloud," in Pervasive Computing (ICPC), 2015 International Conference on, 2015, pp. 1–5

  44. A. V. Do, J. Chen, C. Wang, Y. C. Lee, A. Y. Zomaya, and B. B. Zhou, "Profiling applications for virtual machine placement in clouds," in Cloud Computing (CLOUD), 2011 IEEE International Conference on, 2011, pp. 660–667

  45. T. Setzer and A. Stage, "Decision support for virtual machine reassignments in enterprise data centers," in Network Operations and Management Symposium Workshops (NOMS Wksps), 2010 IEEE/IFIP, 2010, pp. 88–94

  46. Fu, X., Zhou, C.: Virtual machine selection and placement for dynamic consolidation in cloud computing environment. Frontiers of Computer Science. 9, 322–330 (2015)

    MathSciNet  Google Scholar 

  47. Xu, B., Peng, Z., Xiao, F., Gates, A.M., Yu, J.-P.: Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft. Comput. 19, 2265–2273 (2015)

    Google Scholar 

  48. Kinger, S., Kumar, R., Sharma, A.: Prediction based proactive thermal virtual machine scheduling in green clouds. Sci. World J. 2014, (2014)

  49. Gaggero, M., Caviglione, L.: Model predictive control for energy-efficient, quality-aware, and secure virtual machine placement. IEEE Trans. Autom. Sci. Eng. 1–13 (2018)

  50. M. Naderpour, "a Fuzzy Virtual Machine Workload Prediction Method for Cloud Environments," in IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), 2017

    Google Scholar 

  51. J. Lopez, N. Kushik, and D. Zeghlache, "Quality Estimation of Virtual Machine Placement in Cloud Infrastructures," in IFIP International Conference on Testing Software and Systems, 2017, pp. 213–229

  52. H. Teyeb, N. B. Hadj-Alouane, and S. Tata, "Network-Aware Stochastic Virtual Machine Placement in Geo-Distributed Data Centers," in OTM Confederated International Conferences" On the Move to Meaningful Internet Systems", 2017, pp. 37–44

  53. Versick, D., Waßmann, I., Tavangarian, D.: Power consumption estimation of CPU and peripheral components in virtual machines. ACM SIGAPP Applied Computing Review. 13, 17–25 (2013)

    Google Scholar 

  54. Kecskemeti, G., Nemeth, Z., Kertesz, A., Ranjan, R.: Cloud workload prediction based on workflow execution time discrepancies. Clust. Comput. 1–19 (2018)

  55. D.F. Kirchoff, M. Xavier, J. Mastella, and De C.A. Rose, "A Preliminary Study of Machine Learning Workload Prediction Techniques for Cloud Applications," in 2019 27th Euromicro international conference on parallel, Distributed and Network-Based Processing (PDP), 2019, pp. 222–227

  56. Shaw, R., Howley, E., Barrett, E.: An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions. Simul. Model. Pract. Theory. 93, 322–342 (2019)

    Google Scholar 

  57. Tarahomi, M., Izadi, M.: A prediction-based and power-aware virtual machine allocation algorithm in three-tier cloud data centers. Int. J. Commun. Syst. 32, e3870 (2019)

    Google Scholar 

  58. D. Minarolli and B. Freisleben, "Cross-correlation prediction of resource demand for virtual machine resource allocation in clouds," in Computational Intelligence, Communication Systems and Networks (CICSyN), 2014 Sixth International Conference on, 2014, pp. 119–124

  59. Hammer, H.L., Yazidi, A., Begnum, K.: An inhomogeneous hidden Markov model for efficient virtual machine placement in cloud computing environments. J. Forecast. 36, 407–420 (2017)

    MathSciNet  Google Scholar 

  60. Melhem, S.B., Agarwal, A., Goel, N., Zaman, M.: Markov prediction model for host load detection and VM placement in live migration. IEEE Access. 6, 7190–7205 (2018)

    Google Scholar 

  61. Q. Gao, P. Tang, T. Deng, and T. Wo, "Virtualrank: A prediction based load balancing technique in virtual computing environment," in Services (SERVICES), 2011 IEEE World Congress on, 2011, pp. 247–256

  62. Z. Han, H. Tan, G. Chen, R. Wang, Y. Chen, and F. C. Lau, "Dynamic virtual machine management via approximate markov decision process," in INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE, 2016, pp. 1–9

  63. Chen, T., Zhu, Y., Gao, X., Kong, L., Chen, G., Wang, Y.: Improving resource utilization via virtual machine placement in data center networks. Mobile Networks and Applications. 23, 227–238 (2018)

    Google Scholar 

  64. Bala, A., Chana, I.: Prediction-based proactive load balancing approach through VM migration. Eng. Comput. 32, 581–592 (2016)

    Google Scholar 

  65. F. Caglar, S. Shekhar, and A. Gokhale, "iPlace: An intelligent and tunable power-and performance-aware virtual machine placement technique for cloud-based real-time applications," in Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), 2014 IEEE 17th International Symposium on, 2014, pp. 48–55

  66. T. Chen, Y. Zhu, X. Gao, L. Kong, G. Chen, and Y. Wang, "Correlation-aware virtual machine placement in data center networks," in Cloud Computing, Security, Privacy in New Computing Environments, ed: Springer, 2016, pp. 22–32

  67. Ghobaei-Arani, M., Shamsi, M., Rahmanian, A.A.: An efficient approach for improving virtual machine placement in cloud computing environment. J. Experimental Theoretical Artificial Intelligence. 29, 1149–1171 (2017)

    Google Scholar 

  68. Garg, S.K., Toosi, A.N., Gopalaiyengar, S.K., Buyya, R.: SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J. Netw. Comput. Appl. 45, 108–120 (2014)

    Google Scholar 

  69. F. Machida, M. Kawato, and Y. Maeno, "Redundant virtual machine placement for fault-tolerant consolidated server clusters," in Network Operations and Management Symposium (NOMS), 2010 IEEE, 2010, pp. 32–39

  70. R. N. Calheiros, R. Ranjan, and R. Buyya, "Virtual machine provisioning based on analytical performance and QoS in cloud computing environments," in Parallel processing (ICPP), 2011 international conference on, 2011, pp. 295–304

  71. Q. Zia Ullah, S. Hassan, and G. M. Khan, "Adaptive resource utilization prediction system for infrastructure as a service cloud," Computational Intelligence and Neuroscience, vol. 2017, 2017

  72. W. Wei, X. Wei, T. Chen, X. Gao, and G. Chen, "Dynamic correlative VM placement for quality-assured cloud service," in Communications (ICC), 2013 IEEE International Conference on, 2013, pp. 2573–2577

  73. Fu, X., Zhou, C.: Predicted affinity based virtual machine placement in cloud computing environments. IEEE Transactions on Cloud Computing. 1–1 (2017)

  74. H. Goudarzi and M. Pedram, "Energy-efficient virtual machine replication and placement in a cloud computing system," in Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, 2012, pp. 750–757

  75. D. Grygorenko, S. Farokhi, and I. Brandic, "Cost-Aware VM Placement across Distributed DCs using Bayesian Networks," in International Conference on Grid Economics and Business Models, 2015, pp. 32–48

  76. Hong, H.-J., Chen, D.-Y., Huang, C.-Y., Chen, K.-T., Hsu, C.-H.: Placing virtual machines to optimize cloud gaming experience. IEEE Transactions on Cloud Computing. 3, 42–53 (2015)

    Google Scholar 

  77. S. Imai, S. Patterson, and C. A. Varela, "Uncertainty-aware elastic virtual machine scheduling for stream processing systems," in 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2018, pp. 62–71

  78. C. S. Verma, V. D. Reddy, G. Gangadharan, and A. Negi, "Energy Efficient Virtual Machine Placement in Cloud Data Centers Using Modified Intelligent Water Drop Algorithm," in Signal-Image Technology & Internet-Based Systems (SITIS), 2017 13th International Conference on, 2017, pp. 13–20

  79. Kim, N., Cho, J., Seo, E.: Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. Futur. Gener. Comput. Syst. 32, 128–137 (2014)

    Google Scholar 

  80. Shaw, S.B., Singh, A.K.: Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center. Comput. Electr. Eng. 47, 241–254 (2015)

    Google Scholar 

  81. Ranjbari, M., Torkestani, J.A.: A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers. J. Parallel and Distributed Computing. 113, 55–62 (2018)

    Google Scholar 

  82. 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. 24, 1397–1420 (2012)

    Google Scholar 

  83. N. Bobroff, A. Kochut, and K. Beaty, "Dynamic placement of virtual machines for managing sla violations," in Integrated Network Management, 2007. IM'07. 10th IFIP/IEEE International Symposium on, 2007, pp. 119–128

  84. Li, X., Garraghan, P., Jiang, X., Wu, Z., Xu, J.: Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. IEEE Transactions on Parallel and Distributed Systems. 29, 1317–1331 (2018)

    Google Scholar 

  85. N. A. Singh and M. Hemalatha, "Reduce energy consumption through virtual machine placement in cloud data centre," in Mining Intelligence and Knowledge Exploration, ed: Springer, 2013, pp. 466–474

  86. K. Sato, M. Samejima, and N. Komoda, "Dynamic optimization of virtual machine placement by resource usage prediction," in Industrial Informatics (INDIN), 2013 11th IEEE International Conference on, 2013, pp. 86–91

  87. Tang, Z., Mo, Y., Li, K., Li, K.: Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing environment. J. Supercomput. 70, 1279–1296 (2014)

    Google Scholar 

  88. Han, G., Que, W., Jia, G., Shu, L.: An efficient virtual machine consolidation scheme for multimedia cloud computing. Sensors. 16, 246 (2016)

    Google Scholar 

  89. Nguyen, T.H., Di Francesco, M., Yla-Jaaski, A.: Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans. Serv. Comput. 1 (2017)

  90. M. Seddigh, H. Taheri, and S. Sharifian, "Dynamic prediction scheduling for virtual machine placement via ant colony optimization," in Signal Processing and Intelligent Systems Conference (SPIS), 2015, 2015, pp. 104–108

  91. Duan, H., Chen, C., Min, G., Wu, Y.: Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems. Futur. Gener. Comput. Syst. 74, 142–150 (2017)

    Google Scholar 

  92. Jia, J., Chen, N., Zhang, S.: "Forecasting Availability of Virtual Machine Based on Grey-Exponential Curve Combination Model," in International Conference on Security, pp. 297–310. Privacy and Anonymity in Computation, Communication and Storage (2016)

    Google Scholar 

  93. Xu, X., Zhang, Q., Maneas, S., Sotiriadis, S., Gavan, C., Bessis, N.: VMSAGE: a virtual machine scheduling algorithm based on the gravitational effect for green cloud computing. Simul. Model. Pract. Theory. (2018)

  94. Tseng, F.-H., Wang, X., Chou, L.-D., Chao, H.-C., Leung, V.C.: Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Syst. J. 12, 1688–1699 (2018)

    Google Scholar 

  95. J.-J. Jheng, F.-H. Tseng, H.-C. Chao, and L.-D. Chou, "A novel VM workload prediction using Grey Forecasting model in cloud data center," in Information Networking (ICOIN), 2014 International Conference on, 2014, pp. 40–45

  96. J. Cao, Y. Wu, and M. Li, "Energy efficient allocation of virtual machines in cloud computing environments based on demand forecast," in International Conference on Grid and Pervasive Computing, 2012, pp. 137–151

  97. Z. Zhang, L. Xiao, Y. Li, and L. Ruan, "A VM-based resource management method using statistics," in Parallel and Distributed Systems (ICPADS), 2012 IEEE 18th International Conference on, 2012, pp. 788–793

  98. Subirats, J., Guitart, J.: Assessing and forecasting energy efficiency on cloud computing platforms. Futur. Gener. Comput. Syst. 45, 70–94 (2015)

    Google Scholar 

  99. Q. Li, Q. Yang, Q. He, and K. S. Kwak, "Profit-maximizing virtual machine provisioning based on workload prediction in computing cloud," KSII Transactions on Internet and Information Systems (TIIS), vol. 9, pp. 4950–4966, 2015

  100. J. Jiang, X. Zhao, Y. Wu, and W. Zheng, "I/O-Conscious and Prediction-Enabled Virtual Machines Scheduling," in Computer and Information Technology (CIT), 2016 IEEE International Conference on, 2016, pp. 760–767

  101. D. Dong and J. Herbert, "Energy efficient vm placement supported by data analytic service," in Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on, 2013, pp. 648–655

  102. C. C. T. Mark, D. Niyato, and T. Chen-Khong, "Evolutionary optimal virtual machine placement and demand forecaster for cloud computing," in Advanced Information Networking and Applications (AINA), 2011 IEEE International Conference on, 2011, pp. 348–355

  103. 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, e3537 (2018)

    Google Scholar 

  104. W. Li, J. Tordsson, and E. Elmroth, "Virtual machine placement for predictable and time-constrained peak loads," in International Workshop on Grid Economics and Business Models, 2011, pp. 120–134

  105. J. Peng, Y. Wang, G. Chen, L. You, F. Cheng, and W. Lv, "A Virtual Machine Dynamic Adjustment Strategy Based on Load Forecasting," in International Conference on Algorithms and Architectures for Parallel Processing, 2018, pp. 538–550

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Masdari.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masdari, M., Zangakani, M. Green Cloud Computing Using Proactive Virtual Machine Placement: Challenges and Issues. J Grid Computing 18, 727–759 (2020). https://doi.org/10.1007/s10723-019-09489-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-019-09489-9

Keywords

Navigation