Skip to main content

Advertisement

SpringerLink
Log in

The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. www.sciencedirect.com.

  2. www.citeseerx.ist.psu.edu.

  3. www.dl.acm.org.

  4. www.ieeexplore.ieee.org.

  5. www.scholar.google.com.

References

  1. Seyfollahi, A., Ghaffari, A.: A lightweight load balancing and route minimizing solution for routing protocol for low-power and lossy networks. Comput. Netw. 179, 107368 (2020)

    Google Scholar 

  2. Meng, Q., Zhang, J.: Optimization and application of artificial intelligence routing algorithm. Clust. Comput. 22(4), 8747–8755 (2019)

    Google Scholar 

  3. Hayyolalam, V., Kazem, A.A.P.: A systematic literature review on QoS-aware service composition and selection in cloud environment. J. Netw. Comput. Appl. 110, 52–74 (2018)

    Google Scholar 

  4. Li, S., Da Xu, L., Zhao, S.: 5G Internet of Things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018)

    Google Scholar 

  5. Moniruzzaman, M., et al.: Blockchain for smart homes: review of current trends and research challenges. Comput. Electr. Eng. 83, 106585 (2020)

    Google Scholar 

  6. Ghomi, E.J., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88, 50–71 (2017)

    Google Scholar 

  7. Ghobaei-Arani, M., Khorsand, R., Ramezanpour, M.: An autonomous resource provisioning framework for massively multiplayer online games in cloud environment. J. Netw. Comput. Appl. 142, 76–97 (2019)

    Google Scholar 

  8. Sehgal, N.K., Bhatt, P.C.: Cloud Computing. Springer, Cham (2018)

    Google Scholar 

  9. Kumar, E.S., Vengatesan, K.: Trust based resource selection with optimization technique. Clust. Comput. 22(1), 207–213 (2019)

    Google Scholar 

  10. Magid, S.A., Petrini, F., Dezfouli, B.: Image classification on IoT edge devices: profiling and modeling. Clust. Comput. 23(1), 1–19 (2019)

    Google Scholar 

  11. Nikoui, T.S., Rahmani, A.M., Tabarsaied, H.: Data management in fog computing. In: Fog and Edge Computing: Principles and Paradigms, pp. 171–190. Wiley, Hoboken (2019)

  12. Kunwar, V., et al.: Load balancing in cloud—a systematic review. In: Big Data Analytics, pp. 583–593. Springer, Singapore (2018)

  13. Mahapatra, P.K., et al.: Security model for preserving privacy of image in cloud. In: Advances in Data Science and Management, pp. 247–256. Springer, Singapore (2020)

  14. Sundararaj, V.: Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel. Pers. Commun. 104(1), 173–197 (2019)

    Google Scholar 

  15. Darzanos, G., Koutsopoulos, I., Stamoulis, G.D.: Cloud federations: economics, games and benefits. IEEE/ACM Trans. Netw. 27(5), 2111–2124 (2019)

    Google Scholar 

  16. Sohaib, O., et al.: Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method. Comput. Ind. Eng. 132, 47–58 (2019)

    Google Scholar 

  17. Nzanywayingoma, F., Yang, Y.: Efficient resource management techniques in cloud computing environment: a review and discussion. Int. J. Comput. Appl. 41(3), 165–182 (2019)

    Google Scholar 

  18. Oliveira, T., et al.: Understanding SaaS adoption: the moderating impact of the environment context. Int. J. Inf. Manag. 49, 1–12 (2019)

    Google Scholar 

  19. Costache, S., et al.: Resource management in cloud platform as a service systems: analysis and opportunities. J. Syst. Softw. 132, 98–118 (2017)

    Google Scholar 

  20. Haghighi, M.A., Maeen, M., Haghparast, M.: An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing IaaS platforms. Wirel. Pers. Commun. 104(4), 1367–1391 (2019)

    Google Scholar 

  21. Chen, T., et al.: Improving resource utilization via virtual machine placement in data center networks. Mob. Netw. Appl. 23(2), 227–238 (2018)

    Google Scholar 

  22. 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. (2020). https://doi.org/10.1007/s10586-020-03152-9

    Article  Google Scholar 

  23. Piraghaj, S.F., et al.: Virtual machine customization and task mapping architecture for efficient allocation of cloud data center resources. Comput. J. 59(2), 208–224 (2016)

    Google Scholar 

  24. Bermejo, B., Juiz, C.: Virtual machine consolidation: a systematic review of its overhead influencing factors. J. Supercomput. 76(1), 324–361 (2020)

    Google Scholar 

  25. Li, Z., et al.: Energy-efficient and quality-aware VM consolidation method. Future Gener. Comput. Syst. 102, 789–809 (2020)

    Google Scholar 

  26. Haghshenas, K., et al.: MAGNETIC: multi-agent machine learning-based approach for energy efficient dynamic consolidation in data centers. IEEE Trans. Serv. Comput. (2019). https://doi.org/10.1109/TSC.2019.2919555

    Article  Google Scholar 

  27. Malekloo, M.-H., Kara, N., El Barachi, M.: An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain. Comput. Inform. Syst. 17, 9–24 (2018)

    Google Scholar 

  28. Xiao, H., Hu, Z., Li, K.: Multi-objective VM consolidation based on thresholds and ant colony system in cloud computing. IEEE Access 7, 53441–53453 (2019)

    Google Scholar 

  29. Abdelsamea, A., et al.: Virtual machine consolidation challenges: a review. Int. J. Innov. Appl. Stud. 8(4), 1504 (2014)

    Google Scholar 

  30. Abadi, R.M.B., Rahmani, A.M., Alizadeh, S.H.: Challenges of server consolidation in virtualized data centers and open research issues: a systematic literature review. J. Supercomput. 76, 1–52 (2019)

    Google Scholar 

  31. Ahmad, R.W., et al.: A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J. Netw. Comput. Appl. 52, 11–25 (2015)

    Google Scholar 

  32. Bermejo, B., Juiz, C., Guerrero, C.: Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance. J. Supercomput. 75(2), 808–836 (2019)

    Google Scholar 

  33. Shirvani, M.H., Rahmani, A.M., Sahafi, A.: A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: taxonomy and challenges. J. King Saud Univ. Comput. Inf. Sci. 32(3), 267–286 (2020)

    Google Scholar 

  34. Abadi, R.M.B., Rahmani, A.M., Alizadeh, S.H.: Server consolidation techniques in virtualized data centers of cloud environments: a systematic literature review. Softw. Pract. Exp. 48(9), 1688–1726 (2018)

    Google Scholar 

  35. Masdari, M., et al.: Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions. Clust. Comput. 23, 1–31 (2019)

    Google Scholar 

  36. Asghari, P., Rahmani, A.M., Javadi, H.H.S.: Internet of Things applications: a systematic review. Comput. Netw. 148, 241–261 (2019)

    Google Scholar 

  37. Pourghebleh, B., Hayyolalam, V., Anvigh, A.A.: Service discovery in the Internet of Things: review of current trends and research challenges. Wirel. Netw. 26(7), 5371–5391 (2020)

    Google Scholar 

  38. Pourghebleh, B., Hayyolalam, V.: A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things. Clust. Comput. 23, 1–21 (2019)

    Google Scholar 

  39. Pourghebleh, B., Wakil, K., Navimipour, N.J.: A comprehensive study on the trust management techniques in the Internet of Things. IEEE Internet Things J. 6(6), 9326–9337 (2019)

    Google Scholar 

  40. Pourghebleh, B., Navimipour, N.J.: Data aggregation mechanisms in the Internet of Things: a systematic review of the literature and recommendations for future research. J. Netw. Comput. Appl. 97, 23–34 (2017)

    Google Scholar 

  41. Hayyolalam, V., et al.: Exploring the state-of-the-art service composition approaches in cloud manufacturing systems to enhance upcoming techniques. Int. J. Adv. Manuf. Technol. 105(1–4), 471–498 (2019)

    Google Scholar 

  42. Pourghebleh, B., Jafari Navimipour, N.: Towards efficient data collection mechanisms in the vehicular ad hoc networks. Int. J. Commun. Syst. 32(5), e3893 (2019)

    Google Scholar 

  43. Hayyolalam, V., Pourghebleh, B., Pourhaji Kazem, A.A.: Trust management of services (TMoS): investigating the current mechanisms. Trans. Emerg. Telecommun. Technol. 31(10), e4063 (2020)

    Google Scholar 

  44. Feller, E., Morin, C., Esnault, A.: A case for fully decentralized dynamic VM consolidation in clouds. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings. 2012. IEEE (2012)

  45. Farahnakian, F., et al.: Energy-aware dynamic VM consolidation in cloud data centers using ant colony system. In: 2014 IEEE 7th International Conference on Cloud Computing. 2014. IEEE (2014).

  46. Matre, P., Silakari, S., Chourasia, U.: Ant colony optimization (ACO) based dynamic VM consolidation for energy efficient cloud computing. Int. J. Comput. Sci. Inf. Secur. 14(8), 345 (2016)

    Google Scholar 

  47. Ferdaus, M.H., et al.: Multi-objective, decentralized dynamic virtual machine consolidation using ACO metaheuristic in computing clouds (2017). arXiv preprint arXiv:1706.06646

  48. Zhang, H., et al.: Workload-aware VM consolidation in cloud based on max–min ant system. In: International Conference on Cloud Computing and Security. 2017. Springer (2017)

  49. Ashraf, A., Porres, I.: Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Int. J. Parallel Emerg. Distrib. Syst. 33(1), 103–120 (2018)

    Google Scholar 

  50. Aryania, A., Aghdasi, H.S., Khanli, L.M.: Energy-aware virtual machine consolidation algorithm based on ant colony system. J. Grid Comput. 16(3), 477–491 (2018)

    Google Scholar 

  51. Liu, F., et al.: A virtual machine consolidation algorithm based on ant colony system and extreme learning machine for cloud data center. IEEE Access 8, 53–67 (2019)

    Google Scholar 

  52. Zheng, Q., et al.: Multi-objective optimization algorithm based on BBO for virtual machine consolidation problem. In: 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS). 2015. IEEE (2015)

  53. Shi, K., et al.: Multi-objective biogeography-based method to optimize virtual machine consolidation. In: Proceedings of the International Conference on Software Engineering and Knowledge Engineering. 2016

  54. Zheng, Q., et al.: Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Future Gener. Comput. Syst. 54, 95–122 (2016)

    Google Scholar 

  55. Jiang, J., et al.: DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model. Future Gener. Comput. Syst. 74, 132–141 (2017)

    Google Scholar 

  56. Li, Z., et al.: Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Future Gener. Comput. Syst. 80, 139–156 (2018)

    Google Scholar 

  57. Joshi, S., Kaur, S.: Cuckoo search approach for virtual machine consolidation in cloud data centre. In: International Conference on Computing, Communication and Automation. 2015. IEEE (2015)

  58. Naik, B.B., et al.: Developing a cloud computing data center virtual machine consolidation based on multi-objective hybrid fruit-fly cuckoo search algorithm. In: 2018 IEEE 5G World Forum (5GWF). 2018. IEEE (2018)

  59. Perumal, B., Murugaiyan, A.: A firefly colony and its fuzzy approach for server consolidation and virtual machine placement in cloud datacenters. Adv. Fuzzy Syst. (2016). https://doi.org/10.1155/2016/6734161

    Article  MathSciNet  Google Scholar 

  60. Yavari, M., Rahbar, A.G., Fathi, M.H.: Temperature and energy-aware consolidation algorithms in cloud computing. J. Cloud Comput. 8(1), 1–16 (2019)

    Google Scholar 

  61. Wu, Q., Ishikawa, F.: Heterogeneous virtual machine consolidation using an improved grouping genetic algorithm. In: IEEE 17th International Conference on High Performance Computing and Communications. 2015. IEEE (2015)

  62. Wu, Q., et al.: Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters. IEEE Trans. Serv. Comput. 12(4), 550–563 (2016)

    Google Scholar 

  63. Theja, P.R., Babu, S.K.: Evolutionary computing based on QoS oriented energy efficient VM consolidation scheme for large scale cloud data centers. Cybern. Inf. Technol. 16(2), 97–112 (2016)

    MathSciNet  Google Scholar 

  64. Arianyan, E., Taheri, H., Sharifian, S.: Multi target dynamic VM consolidation in cloud data centers using genetic algorithm. J. Inf. Sci. Eng. 32(6), 1575–1593 (2016)

    MathSciNet  Google Scholar 

  65. Riahi, M., Krichen, S.: A multi-objective decision support framework for virtual machine placement in cloud data centers: a real case study. J. Supercomput. 74(7), 2984–3015 (2018)

    Google Scholar 

  66. Yousefipour, A., Rahmani, A.M., Jahanshahi, M.: Energy and cost-aware virtual machine consolidation in cloud computing. Softw. Pract. Exp. 48(10), 1758–1774 (2018)

    Google Scholar 

  67. Fathi, M.H., Khanli, L.M.: Consolidating VMs in green cloud computing using harmony search algorithm. In: Proceedings of the 2018 International Conference on Internet and e-Business (2018)

  68. Kim, M., Hong, J., Kim, W.: An efficient representation using harmony search for solving the virtual machine consolidation. Sustainability 11(21), 6030 (2019)

    Google Scholar 

  69. Dashti, S.E., Rahmani, A.M.: Dynamic VMs placement for energy efficiency by PSO in cloud computing. J. Exp. Theor. Artif. Intell. 28(1–2), 97–112 (2016)

    Google Scholar 

  70. Li, H., et al.: Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3), 303–317 (2016)

    MathSciNet  MATH  Google Scholar 

  71. Marotta, A., Avallone, S.: A simulated annealing based approach for power efficient virtual machines consolidation. In: 2015 IEEE 8th International Conference on Cloud Computing. 2015. IEEE (2015)

  72. Rajabzadeh, M., Haghighat, A.T.: Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers. J. Supercomput. 73(5), 2001–2017 (2017)

    Google Scholar 

  73. Nasim, R., Kassler, A.J.: A robust Tabu Search heuristic for VM consolidation under demand uncertainty in virtualized datacenters. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 2017. IEEE (2017)

  74. Abdel-Basset, M., Abdle-Fatah, L., Sangaiah, A.K.: An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust. Comput. 22(4), 8319–8334 (2019)

    Google Scholar 

  75. Al-Moalmi, A., et al.: A whale optimization system for energy-efficient container placement in data centers. Expert Syst. Appl. 164, 113719 (2021)

    Google Scholar 

  76. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Google Scholar 

  77. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)

    MathSciNet  MATH  Google Scholar 

  78. Mareli, M., Twala, B.: An adaptive Cuckoo search algorithm for optimisation. Appl. Comput. Inform. 14(2), 107–115 (2018)

    Google Scholar 

  79. Zhang, T., Geem, Z.W.: Review of harmony search with respect to algorithm structure. Swarm Evol. Comput. 48, 31–43 (2019)

    Google Scholar 

  80. Zhang, W., et al.: Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy 163, 191–207 (2018)

    Google Scholar 

  81. Xue, X., Chen, J.: Using Compact Evolutionary Tabu Search algorithm for matching sensor ontologies. Swarm Evol. Comput. 48, 25–30 (2019)

    Google Scholar 

  82. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Google Scholar 

  83. Hayyolalam, V., Kazem, A.A.P.: Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103249 (2020)

    Google Scholar 

  84. Seyfollahi, A., Ghaffari, A.: Reliable data dissemination for the Internet of Things using Harris Hawks optimization. Peer-to-Peer Netw. Appl. 13(6), 1886–1902 (2020)

    Google Scholar 

  85. Abualigah, L., et al.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021)

    MathSciNet  MATH  Google Scholar 

  86. Kaveh, A., Zaerreza, A.: Shuffled Shepherd optimization method: a new meta-heuristic algorithm. Eng. Comput. 37(7), 2357–2389 (2020)

    Google Scholar 

  87. Yapici, H., Cetinkaya, N.: A new meta-heuristic optimizer: pathfinder algorithm. Appl. Soft Comput. 78, 545–568 (2019)

    Google Scholar 

  88. Abualigah, L., Diabat, A.: A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust. Comput. 24, 1–19 (2020)

    Google Scholar 

  89. Hayyolalam, V., Kazem, A.A.P.: QoS-aware optimization of cloud service composition using symbiotic organisms search algorithm. J. Intell. Proced. Electr. Technol. 8(32), 29–38 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Behrouz Pourghebleh, Amir Aghaei Anvigh & Behnaz Mohammadi

  2. Young Researchers and Elite Club, Urmia Branch, Islamic Azad University, Urmia, Iran

    Behrouz Pourghebleh

  3. College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA

    Amir Reza Ramtin

Authors
  1. Behrouz Pourghebleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Aghaei Anvigh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir Reza Ramtin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behnaz Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behrouz Pourghebleh.

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

Pourghebleh, B., Aghaei Anvigh, A., Ramtin, A.R. et al. The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments. Cluster Comput 24, 2673–2696 (2021). https://doi.org/10.1007/s10586-021-03294-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03294-4

Keywords

Search

Navigation