Skip to main content

Advertisement

Springer Nature Link
Log in

An intelligent virtual machine allocation optimization model for energy-efficient and reliable cloud environment

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The exponential growth of cloud computing has brought increased attention to energy efficiency in data centers. However, fluctuating resource demands and fixed virtual machine (VM) sizes lead to excessive energy consumption, inefficient resource utilization, and load imbalances. While dynamic VM consolidation mitigates these issues by reducing the number of active physical machines (PM), frequent consolidation can compromise system reliability, as VMs may be assigned to unreliable PMs. An effective resource management strategy is therefore essential for balancing energy efficiency and reliability in cloud data centers. This paper presents a novel resource prediction-based VM allocation approach that significantly reduces energy consumption while enhancing system reliability. The core innovation lies in optimizing a feed-forward neural network using the self-adaptive differential evolution algorithm, which integrates multi-dimensional learning and global exploration. Unlike traditional gradient descent algorithms, this method searches for the global best solution, offering more accurate and robust predictions of future resource usage. These proactive resource estimations enable fault-tolerant and reliable VM management, preventing system failures and improving overall performance. Evaluated with the Google cluster dataset, the proposed model outperforms existing methods, delivering remarkable reductions in power consumption (up to 44.81%) and the number of active PMs (up to 64.73%). Additionally, the system’s reliability improves by 65.25%, demonstrating the effectiveness of the approach in achieving energy-efficient and fault-tolerant cloud data center management.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Algorithm 3
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

No datasets were generated or analyzed during the current study.

References

  1. Mell P, Grance T et al (2011) The nist definition of cloud computing

  2. Lin W, Luo X, Li C, Liang J, Wu G, Li K (2023) An energy-efficient tuning method for cloud servers combining DVFS and parameter optimization. IEEE Trans Cloud Comput

  3. Gupta A, Namasudra S, Kumar P (2024) A secure VM live migration technique in a cloud computing environment using blowfish and blockchain technology. J Supercomput 1–24

  4. Swain SR, Saxena D, Kumar J, Singh AK, Lee C-N (2024) An intelligent straggler traffic management framework for sustainable cloud environments. IEEE Trans Sustain Comput

  5. Hiremath TC, Rekha K (2023) Energy efficient data migration concerning interoperability using optimized deep learning in container-based heterogeneous cloud computing. Adv Eng Softw 183:103496

    Article  Google Scholar 

  6. Yang W, Zhao M, Li J, Zhang X (2024) Energy-efficient DAG scheduling with DVFS for cloud data centers. J Supercomput 80(10):14799–14823

    Article  Google Scholar 

  7. Kaur G, Bala A, Chana I (2019) An intelligent regressive ensemble approach for predicting resource usage in cloud computing. J Parallel Distrib Comput 123:1–12

    Article  Google Scholar 

  8. Sayadnavard MH, Toroghi Haghighat A, Rahmani AM (2019) A reliable energy-aware approach for dynamic virtual machine consolidation in cloud data centers. J Supercomput 75(4):2126–2147

    Article  Google Scholar 

  9. Shang L, Peh L-S, Jha NK (2003) Dynamic voltage scaling with links for power optimization of interconnection networks. In: The Ninth International Symposium on High-Performance Computer Architecture. HPCA-9 2003. Proceedings. IEEE, pp 91–102

  10. Nawrocki P, Grzywacz M, Sniezynski B (2021) Adaptive resource planning for cloud-based services using machine learning. J Parallel Distrib Comput 152:88–97

    Article  Google Scholar 

  11. Swain SR, Saxena D, Kumar J, Singh AK, Lee C-N (2024) An intelligent straggler traffic management framework for sustainable cloud environments. IEEE Trans Sustain Comput 1–13. https://doi.org/10.1109/TSUSC.2024.3393357

  12. Rostami S, Broumandnia A, Khademzadeh A (2024) An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithm. J Supercomput 80(6):7812–7848

    Article  Google Scholar 

  13. Kaviarasan R, Harikrishna P, Arulmurugan A (2022) Load balancing in cloud environment using enhanced migration and adjustment operator based monarch butterfly optimization. Adv Eng Softw 169:103128

    Article  Google Scholar 

  14. Saxena D, Gupta I, Kumar J, Singh AK, Wen X (2021) A secure and multi-objective virtual machine placement framework for cloud data center. IEEE Syst J

  15. Sharma NK, Reddy GRM (2016) Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans Serv Comput 12(1):158–171

    Article  Google Scholar 

  16. Singh AK, Kumar J (2019) Secure and energy aware load balancing framework for cloud data centre networks. Electron Lett 55(9):540–541

    Article  Google Scholar 

  17. Tseng F-H, Wang X, Chou L-D, Chao H-C, Leung VC (2017) Dynamic resource prediction and allocation for cloud data center using the multi-objective genetic algorithm. IEEE Syst J 12(2):1688–1699

    Article  Google Scholar 

  18. Han J, Zang W, Chen S, Yu M (2017) Reducing security risks of clouds through virtual machine placement. In: IFIP Annual Conference on Data and Applications Security and Privacy. Springer, pp 275–292

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

    Article  Google Scholar 

  20. Saxena D, Kumar J, Singh AK, Schmid S (2023) Performance analysis of machine learning centered workload prediction models for cloud. IEEE Trans Parallel Distrib Syst 34(4):1313–1330

    Article  Google Scholar 

  21. Wang B, Liu F, Lin W, Ma Z, Xu D (2021) Energy-efficient collaborative optimization for VM scheduling in cloud computing. Comput Netw 201:108565

    Article  Google Scholar 

  22. Saxena D, Singh AK, Buyya R (2021) OP-MLB: An online VM prediction based multi-objective load balancing framework for resource management at cloud datacenter. IEEE Trans Cloud Comput

  23. Peake J, Amos M, Costen N, Masala G, Lloyd H (2022) PACO-VMP: parallel ant colony optimization for virtual machine placement. Futur Gener Comput Syst 129:174–186

    Article  Google Scholar 

  24. Xuejie Z, Zhijian W, Feng X (2013) Reliability evaluation of cloud computing systems using hybrid methods. Intell Autom Soft Comput 19(2):165–174

    Article  Google Scholar 

  25. Sharma Y, Javadi B, Si W, Sun D (2016) Reliability and energy efficiency in cloud computing systems: survey and taxonomy. J Netw Comput Appl 74:66–85

    Article  Google Scholar 

  26. Zhou A, Wang S, Zheng Z, Hsu C-H, Lyu MR, Yang F (2014) On cloud service reliability enhancement with optimal resource usage. IEEE Trans Cloud Comput 4(4):452–466

    Article  Google Scholar 

  27. Azimzadeh F, Biabani F (2017) Multi-objective job scheduling algorithm in cloud computing based on reliability and time. In: 2017 3th International Conference on Web Research (ICWR). IEEE, pp 96–101

  28. Jangiti S, Sri Ram E, Shankar Sriram V (2019) Aggregated rank in first-fit-decreasing for green cloud computing, pp 545–555

  29. Sayadnavard MH, Haghighat AT, Rahmani AM (2021) A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers. Eng Sci Technol Int J

  30. Iorio AW, Li X (2004) Solving rotated multi-objective optimization problems using differential evolution. In: Australasian Joint Conference on Artificial Intelligence. Springer, pp 861–872

  31. Zhang L, Chang H, Xu R (2012) Equal-width partitioning roulette wheel selection in genetic algorithm. In: 2012 Conference on Technologies and Applications of Artificial Intelligence. IEEE, pp 62–67

  32. Amazon (1999) Amazon EC2 instances. https://aws.amazon.com/ec2/instance-types/. Accessed 19 Jan 2022 (Online)

  33. Reiss C, Wilkes J, Hellerstein JL (2019) Google cluster-usage traces: format+ schema. Google Inc., White Paper 1

  34. Shirvastava S, Dubey R, Shrivastava M (2017) Best fit based VM allocation for cloud resource allocation. Int J Comput Appl 158(9)

  35. Jung G, Hiltunen MA, Joshi KR, Schlichting RD, Pu C (2010) Mistral: dynamically managing power, performance, and adaptation cost in cloud infrastructures. In: 2010 IEEE 30th International Conference on Distributed Computing Systems. IEEE, pp 62–73

Download references

Author information

Author notes

  1. Anshu Parashar, Ashutosh Kumar Singh, and Chung Nan Lee contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Kurukshetra, Haryana, 136119, India

    Smruti Rekha Swain, Anshu Parashar & Ashutosh Kumar Singh

  2. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, 804201, Kaohsiung, Taiwan

    Chung Nan Lee

Authors
  1. Smruti Rekha Swain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anshu Parashar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashutosh Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chung Nan Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Smruti Rekha Swain was involved in conceptualization, methodology, design of the work, software, analysis, writing—original draft, visualization, investigation, and implementation. Anshu Parashar and Ashutosh Kumar Singh took part in conceptualization, visualization, research gap finding, and writing—review and editing. Chung Nan Lee participated in conceptualization, visualization, research gap finding, and writing—review.

Corresponding author

Correspondence to Smruti Rekha Swain.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Swain, S.R., Parashar, A., Singh, A.K. et al. An intelligent virtual machine allocation optimization model for energy-efficient and reliable cloud environment. J Supercomput 81, 237 (2025). https://doi.org/10.1007/s11227-024-06734-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06734-1

Keywords