Skip to main content

Advertisement

SpringerLink
Log in

An Energy-Efficient VNE Algorithm Based on Bidirectional Long Short-Term Memory

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

Abstract

A bidirectional long short-term memory (Bi-LSTM) algorithm is proposed to resolve the problem of energy-efficient virtual network embedding. In the VNE process, a large number of attributes can provide information for efficient embedding. This paper divides them into three categories: “network characteristics”, “embedding sequence” and “task type”, and comprehensively analyzes their influence on the embedded performance of virtual networks. This study uses a graph convolutional network (GCN) to extract the network characteristics of virtual and substrate networks. By this approach, we embedded the network-topology graph containing nodes, links, and topological associations onto the input set of the GCN, which rapidly extracts network features. We then used the network features as the model input for the Bi-LSTM neural network to integrate historical and future embedding sequences into the training model. In this process, in conjunction with meta-reinforcement learning to accumulate the experience of various virtual-network tasks, we systematically adjusted model parameters and thus achieved the automatic tuning of neural networks. Simulation results show that when compared to similar existing algorithms. The proposed algorithm improves the acceptance ratio, average ratio of revenue and cost, and reduces the network energy consumption after integrating the network characteristics, embedding sequence, and task type.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Wang, A., Iyer, M., Dutta, R., Rouskas, G.N., Baldine, l: Network virtualization: technologies, perspectives, and frontiers. J. Lightwave Technol. 31(4), 523–537 (2013). https://doi.org/10.1109/JLT.2012.2213796

    Article  Google Scholar 

  2. Chowdhury, N.M.M.K., Boutaba, R.: Network virtualization: state of the art and research challenges. IEEE Commun. Mag. 47(7), 20–26 (2009). https://doi.org/10.1109/MCOM.2009.5183468

    Article  Google Scholar 

  3. Chowdhury, N.M.M.K., Boutaba, R.: A survey of network virtualization. Comput. Netw. 54(5), 862–876 (2010). https://doi.org/10.1016/j.comnet.2009.10.017

    Article  MATH  Google Scholar 

  4. Fischer, A., Botero, J.F., Beck, M.T., Meer, H.D., Hesselbach, X.: Virtual network embedding: a survey. IEEE Commun. Surv. Tutor. 15(4), 1888–1906 (2013). https://doi.org/10.1109/SURV.2013.013013.00155

    Article  Google Scholar 

  5. Wang, C., Yuan, Y., Peng, S., Wang, X., Wan, C.: Fair virtual network embedding algorithm with topology pre-configuration. J. Comp. Res. Dev. 54(1), 212–220 (2017). https://doi.org/10.7544/issn1000-1239.2017.20150785

    Article  Google Scholar 

  6. Li, X.L., Guo, C.G., Li, X.Y., Wang, H.: A constraint optimization based mapping method for virtual network. J. Comput. Res. Dev. 49(8), 1601–1610 (2012). https://doi.org/10.1117/12.2011106

    Article  Google Scholar 

  7. Jahani, A., Khanli, L.M., Hagh, M.T., Badamchizadeh, M.A.: EE-CTA: Energy efficient, concurrent and topology-aware virtual network embedding as a multi-objective optimization problem. Comput. Stand Interfaces 66(2019), 103351–103368 (2019). https://doi.org/10.1016/j.csi.2019.04.010

    Article  Google Scholar 

  8. Zhang, P.Y., Yao, H.P., Qiu, C., Liu, Y.: Virtual network embedding using node multiple metrics based on simplified ELECTRE method. IEEE Access 6(2018), 37314–37327 (2018). https://doi.org/10.1109/ACCESS.2018.2847910

    Article  Google Scholar 

  9. Feng, M., Liao, J.X., Wang, J.Y., Qing, S., Qi, Q.: Topology-aware virtual network embedding based on multiple characteristics, In: 2014 IEEE International Conference on Communications. pp. 2956–2962. Piscataway, NJ: IEEE. (2014). https://doi.org/10.1109/ICC.2014.6883774.

  10. Cheng, X., Su, S., Zhang, Z.B., Shuang, K., Yang, F.C., Luo, Y., Wang, J.: Virtual network embedding through topology awareness and optimization. Comput. Netw. 56(6), 1797–1813 (2012). https://doi.org/10.1016/j.comnet.2012.01.022

    Article  Google Scholar 

  11. Jahani, A., Khanli, L.M., Hagh, M.T., Badamchizadeh, M.A.: Green virtual network embedding with supervised self-organizing map. Neurocomputing 351(2019), 60–76 (2019). https://doi.org/10.1016/j.neucom.2019.03.036

    Article  Google Scholar 

  12. Yao, H.P., Chen, X., Li, M.Z., Zhang, P.Y., Wang, L.Y.: A novel reinforcement learning algorithm for virtual network embedding. Neurocomputing 284(2018), 1–9 (2018). https://doi.org/10.1016/j.neucom.2018.01.025

    Article  Google Scholar 

  13. Alexandros, K., Melanie, H.: Meta-learning. Int. J. Artif. Intell. Tools 10(04), 525–554 (2001). https://doi.org/10.1142/s0218213001000647

    Article  MATH  Google Scholar 

  14. Vilalta, R., Drissi, Y.: A Perspective View and Survey of Meta-Learning. Artif. Intell. Rev. 18(2), 77–95 (2002). https://doi.org/10.1023/A:1019956318069

    Article  Google Scholar 

  15. Botero, J.F., Hesselbach, X.: Greener networking in a network virtualization environment. Comput. Netw. 57(9), 2021–2039 (2013). https://doi.org/10.1016/j.comnet.2013.04.004

    Article  Google Scholar 

  16. Chen, X.H., Li, C.Z., Chen, L.Y., Zeng, Z.B., Jiang, Y.L.: Multiple feedback control model and algorithm for energy efficient virtual network embedding. J. Softw. 28(7), 1790–1814 (2017)

    Google Scholar 

  17. He, M.Y., Zhuang, L., Tian, S.K., Wang, G.Q., Zhang, K.L.: Multi-objective virtual network embedding algorithm based on Q-learning and curiosity-driven. EURASIP J. Wirel. Commun. Netw. 2018(1), 150–162 (2018). https://doi.org/10.1186/s13638-018-1170-x

    Article  Google Scholar 

  18. Song, A., Chen, W.-N., Gu, T., Yuan, H., Kwong, S., Zhang, J.: Distributed virtual network embedding system with historical archives and set-based particle swarm optimization. IEEE Trans. Syst. Man Cybernet: Syst. 51(2), 927–942 (2021). https://doi.org/10.1109/TSMC.2018.2884523

    Article  Google Scholar 

  19. Lin, R.P., Luo, S., Wang, H.R., Wang, S.: Energy-aware virtual network embedding in flexi-grid networks. Opt. Express 25(24), 29699–29713 (2017). https://doi.org/10.1364/OE.25.029699

    Article  Google Scholar 

  20. He, M.Y., Zhuang, L., Tian, S.K., Wang, G.Q., Zhang, K.L.: DROI: energy-efficient virtual network embedding algorithm based on dynamic regions of interest. Comput. Netw. 166(2020), 106952–106963 (2020). https://doi.org/10.1016/j.comnet.2019.106952

    Article  Google Scholar 

  21. Heo, D., Lange, S., Kim, H. -G., Choi, H.: Graph neural network based service function chaining for automatic network control. 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 7–12. (2020). https://doi.org/10.23919/APNOMS50412.2020.9236954.

  22. Zhang, P., Wang, C., Kumar, N., Zhang, W., Liu, L.: Dynamic virtual network embedding algorithm based on graph convolution neural network and reinforcement learning. IEEE Internet Things J. (2021). https://doi.org/10.1109/JIOT.2021.3095094

    Article  Google Scholar 

  23. Rkhami, A., Quang Pham, T.A., Hadjadj-Aoul, Y., Outtagarts, A., Rubino, G.: On the Use of Graph Neural Networks for Virtual Network Embedding, 2020 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1-6. (2020). https://doi.org/10.1109/ISNCC49221.2020.9297270.

  24. Habibi, F., Dolati, M., Khonsari, A., Ghaderi, M.: Accelerating Virtual Network Embedding with Graph Neural Networks, 2020 16th International Conference on Network and Service Management (CNSM), pp. 1–9, (2020). https://doi.org/10.23919/CNSM50824.2020.9269128.

  25. Zegura, E.W., Calvert, K.L., Bhattacharjee, S.: How to model an Internet work, In: Proceedings of IEEE INFOCOM '96. Conference on Computer Communications, pp. 1996, 594–602. Piscataway, NJ: IEEE. https://doi.org/10.1109/INFCOM.1996.493353.

  26. Bianchi, F., Presti, F. L.: A Markov reward model based greedy heuristic for the virtual network embedding problem, In: 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systemss, pp. 373-378. Piscataway, NJ: IEEE (2016) https://doi.org/10.1109/MASCOTS.2016.55.

Download references

Funding

This work was supported by the State Grid Corporation of China science and technology project “Key technology and application of new multi-mode intelligent network for State Grid” (No. 5700-202024176A-0-0-00).

Author information

Authors and Affiliations

  1. School of Information and Engineering, Zhengzhou University, Zhengzhou, Henan, China

    Mengyang He, Lei Zhuang, Sijin Yang & Zexi Xu

  2. Information & Telecommunication Co. of State Grid Henan Electric Power Company, Henan, China

    Wencui Li & Jizhao Lu

Authors
  1. Mengyang He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sijin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zexi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wencui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jizhao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Zhuang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

He, M., Zhuang, L., Yang, S. et al. An Energy-Efficient VNE Algorithm Based on Bidirectional Long Short-Term Memory. J Netw Syst Manage 30, 45 (2022). https://doi.org/10.1007/s10922-022-09657-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10922-022-09657-5

Keywords

Search

Navigation