Skip to main content
SpringerLink
Log in

Dynamic resource allocation algorithm of virtual networks in edge computing networks

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

The deployment and allocation of network resources are important in the application of edge computing. As an important resource allocation technology in edge computing, network virtualization faces the challenge of the virtual network mapping problem. Most existing studies are limited to static resource allocation, ignoring the time-varying properties of user resource demands, which results in wasted resources. Since user resource demands vary over time, resource allocation with predictive mechanism is a promising solution. However, there are few studies on the application of predictive algorithm as radial basis function network (RBF) algorithms in virtual network dynamic resource allocation. In addition, due to the excessive use of hidden RBF units, this method suffers from expensive inner product calculations and long training times. In this paper, we propose a dynamic network resource demand predicting algorithm based on the group search optimizer (GSO) and incremental design of the RBF (GSO-INC-RBFDM). In the network mapping, the GSO is first used to optimize the node solution. Then, the incremental design is utilized to eliminate the maximum error value and reduce the inner product calculation and training time by adding the RBF unit one by one. Finally, we apply the improved RBF to predict the user demand and reallocate resources based on the predicted results. Simulation results shows that the GSO-INC-RBFDM demonstrates good performance in terms of the acceptance rate, network cost, link pressure and average revenue compared with traditional algorithms.

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

Similar content being viewed by others

References

  1. Gharaibeh A, Salahuddin MA, Hussini SJ (2017) Smart cities: a survey on data management, security, and enabling technologies. IEEE Commun Surv Tutorials 19(4):2456–2501

    Article  Google Scholar 

  2. Li A, Ye X, Ning H (2017) Thing relation modeling in the internet of things. IEEE Access 5:17117–17125

    Article  Google Scholar 

  3. Du Q, Song H, Zhu X (2019) Social-feature enabled communications among devices toward the smart IoT community. IEEE Commun Mag 57(1):130–137

    Article  Google Scholar 

  4. Shi W, Cao J, Zhang Q (2016) Edge computing: vision and challenges. IEEE Internet Things J 3 (5):637–646

    Article  Google Scholar 

  5. Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39

    Article  Google Scholar 

  6. Shi W, Dustdar S (2016) The promise of edge computing. Computer 49(5):78–81

    Article  Google Scholar 

  7. Hawilo H, Shami A, Mirahmadi M (2014) NFV: State of the art, challenges, and implementation in next generation mobile networks (vEPC). IEEE Netw 28(6):18–26

    Article  Google Scholar 

  8. Alcober s J, Hesselbach X, de la Oliva A (2013) Internet future architectures for network and media independent services and protocols[C]. In: 2013 15th International Conference on Transparent Optical Networks (ICTON), pp 1–4

  9. Houidi I, Louati W, Ameur WB (2011) Virtual network provisioning across multiple substrate networks[J]. Comput Netw 55(4):1011–1023

    Article  Google Scholar 

  10. Cheng X, Su S, Zhang Z, et al. (2011) Virtual network embedding through topology-aware node ranking. ACM SIGCOMM Computer Communication Review 41(2):38–47

    Article  Google Scholar 

  11. Farrell JA, Polycarpou MM (2006) Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, vol 48. Wiley, Hoboken

    Book  Google Scholar 

  12. Martins JF, Pires VF, Pires AJ (2007) Unsupervised neural-network-based algorithm for an on-line diagnosis of three-phase induction motor stator fault. IEEE Trans Ind Electron 54(1):259–264

    Article  Google Scholar 

  13. Zhao Z, Min G, Gao W (2018) Deploying edge computing nodes for large-scale iot: A diversity aware approach[J]. IEEE Internet Things J 5(5):3606–3614

    Article  Google Scholar 

  14. Lischka J, Karl H (2009) A virtual network mapping algorithm based on subgraph isomorphism detection[C]. In: Proceedings of the 1st ACM workshop on Virtualized infrastructure systems and architectures. ACM 2009: pp 81–88

  15. Chowdhury NMMK, Rahman MR, Boutaba R (2009) Virtual network embedding with coordinated node and link mapping[C]//IEEE INFOCOM 783-791

  16. Fiedler M (2011) On resource sharing and careful overbooking for network virtualisation. International Journal of Communication Networks and Distributed Systems 6(3):232–248

    Article  Google Scholar 

  17. Hoeflin D, Reeser P (2012) Quantifying the performance impact of overbooking virtualized resources. 2012 IEEE International Conference on Communications 2012:5523–5527

    Google Scholar 

  18. Zhang S, Qian Z, Wu J, Lu S, Epstein L (2014) Virtual network embedding with opportunistic resource sharing. IEEE Trans Parallel Distrib Syst 25(3):816–827

    Article  Google Scholar 

  19. Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246–257

    Article  Google Scholar 

  20. Ahmed AN, Noor CWM, Allawi MF, et al. (2018) RBF-NN-Based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW). Neural Comput Applic 29(3):889– 899

    Article  Google Scholar 

  21. Yu H, Xie T, Paszczynski S, Wilamowski BM (2011) Advantages of radial basis function networks for dynamic system design. IEEE Trans Ind Electron 58(12):5438–5450

    Article  Google Scholar 

  22. Huang S, Tan KK (2009) Fault detection and diagnosis based on modeling and estimation methods. IEEE Trans Neural Netw 20(5):872–881

    Article  Google Scholar 

  23. Meng K, Dong ZY, Wang DH, Wong KP (2010) A self-adaptive RBF neural network classifier for transformer fault analysis. IEEE Trans Power Syst 25(3):1350–1360

    Article  Google Scholar 

  24. Awad M, Qasrawi I (2018) Enhanced RBF neural network model for time series prediction of solar cells panel depending on climate conditions (temperature and irradiance). Neural Comput Applic 30(6):1757–1768

    Article  Google Scholar 

  25. Li T, Duan S, Liu J, Wang L (2018) An improved design of RBF neural network control algorithm based on spintronic memristor crossbar array. Neural Comput Applic 30(6):1939–1946

    Article  Google Scholar 

  26. Zhou F, Peng H, Ruan W, Wang D, Liu M, Gu Y, Li L (2018) Cubic-RBF-ARX modeling and model-based optimal setting control in head and tail stages of cut tobacco drying process. Neural Comput Applic 30(4):1039–1053

    Article  Google Scholar 

  27. Chng ES, Chen S, Mulgrew B (1996) Gradient radial basis function networks for nonlinear and nonstationary time series prediction. IEEE Trans Neural Netw 7(1):190–194

    Article  Google Scholar 

  28. Karayiannis NB (1999) Reformulated radial basis neural networks trained by gradient descent. IEEE Trans Neural Netw 10(3):657–671

    Article  Google Scholar 

  29. Zhang L, Li K, He H, Irwin GW (2013) A new discrete-continuous algorithm for radial basis function networks construction. IEEE transactions on Neural Networks and Learning Systems 24(11):1785–1798

    Article  Google Scholar 

  30. Zhang L, Li K, Bai EW (2013) A new extension of newton algorithm for nonlinear system modelling using RBF neural networks. IEEE Trans Autom Control 58(11):2929–2933

    Article  MathSciNet  Google Scholar 

  31. Han HG, Qiao JF (2013) A structure optimisation algorithm for feedforward neural network construction. Neurocomputing 99:347–357

    Article  Google Scholar 

  32. Reiner P, Wilamowski BM (2015) Efficient incremental construction of RBF networks using quasi-gradient method. Neurocomputing 150:349–356

    Article  Google Scholar 

  33. Xie T, Yu H, Hewlett J, Rozycki P, Wilamowski B (2012) Fast and efficient second-order method for training radial basis function networks. IEEE Transactions on Neural Networks and Learning Systems 23(4):609–619

    Article  Google Scholar 

  34. Hoori AO, Motai Y (2018) Multicolumn RBF network. IEEE Transactions on Neural Networks and Learning Systems 29(4):766–778

    Article  MathSciNet  Google Scholar 

  35. Chen S, Cowan CF, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2(2):302–309

    Article  Google Scholar 

  36. Huang GB, Saratchandran P, Sundararajan N (2004) An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans Syst Man Cybern B Cybern 34(6):2284–2292

    Article  Google Scholar 

  37. Yu H, Reiner PD, Xie T, Bartczak T, Wilamowski BM (2014) An incremental design of radial basis function networks. IEEE Transactions on Neural Networks and Learning Systems 25(10):1793–1803

    Article  Google Scholar 

  38. Wilamowski BM, Yu H (2010) Improved computation for levenberg”CMarquardt training. IEEE Trans Neural Netw 21(6):930–937

    Article  Google Scholar 

  39. Gu L, Tok DS, Yu DL (2018) Development of adaptive p-step RBF network model with recursive orthogonal least squares training. Neural Comput Applic 29(5):1445–1454

    Article  Google Scholar 

  40. Xiaon XC, Zheng XW (2016) A proposal of survivable virtual network embedding algorithm. Journal of High Speed Networks 22(3):241–251

    Article  Google Scholar 

  41. Internet user behavior data sets named << 2016 China Mobile Internet User Analysis>> from the website http://www.199it.com/archives/434375.html

  42. User Mobile Internet Behavior data sets named << 2018 China Mobile Internet User Behavior Insight Report>> from the website http://www.199it.com/archives/758197.html

Download references

Funding

This study received support from the National Natural Science Foundation of China (61373149, 61672329, 61801278). Shandong Provincial Natural Science Foundation for Young Scholars of China (Grant No. ZR2017QF008), Shandong Provincial scientific research programs in colleges and universities (J18KA310).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, People’s Republic of China

    Xiancui Xiao, Xiangwei Zheng & Tian Jie

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250014, People’s Republic of China

    Xiancui Xiao, Xiangwei Zheng & Tian Jie

  3. Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, People’s Republic of China

    Tian Jie

Authors
  1. Xiancui Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangwei Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tian Jie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangwei Zheng.

Ethics declarations

Conflict of interest

There is no conflict of interest.

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

Xiao, X., Zheng, X. & Jie, T. Dynamic resource allocation algorithm of virtual networks in edge computing networks. Pers Ubiquit Comput 25, 571–586 (2021). https://doi.org/10.1007/s00779-019-01277-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-019-01277-2

Keywords

Search

Navigation