A Multi-Domain VNE Algorithm Based on Load Balancing in the IoT Networks

Abstract

The coordinated development of big data, Internet of Things, cloud computing and other technologies has led to an exponential growth in Internet business. However, the traditional Internet architecture gradually shows a rigid phenomenon due to the binding of the network structure and the hardware. In a high-traffic environment, it has been insufficient to meet people’s increasing service quality requirements. Network virtualization is considered to be an effective method to solve the rigidity of the Internet. Among them, virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a large number of research has focused on the evolutionary algorithm’s masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone-based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time.

This is a preview of subscription content, access via your institution.

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

References

  1. 1.

    Guo H, Liu J (2020) Uav-enhanced intelligent offloading for internet of things at the edge. IEEE Trans Industr Inform 16(4):2737–2746

    Article  Google Scholar 

  2. 2.

    Zhao J, Li Q, Gong Y, Zhang K (2019) Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Trans Veh Technol 68(8):7944–7956

    Article  Google Scholar 

  3. 3.

    Guo H, Liu J, Lv J (2019) Toward intelligent task offloading at the edge. IEEE Netw: 1–7

  4. 4.

    Guo H, Zhang J, Liu J (2019) Fiwi-enhanced vehicular edge computing networks: Collaborative task offloading. IEEE Veh Technol Mag 14(1):45–53

    Article  Google Scholar 

  5. 5.

    Du J, Jiang C, Han Z, Zhang H, Mumtaz S, Ren Y (2019) Contract mechanism and performance analysis for data transaction in mobile social networks. IEEE Trans Netw Sci Eng 6(2):103–115

    Article  Google Scholar 

  6. 6.

    Du J, Jiang C, Zhang H, Ren Y, Guizani M (2018) Auction design and analysis for sdn-based traffic offloading in hybrid satellite-terrestrial networks. IEEE J Sel Areas Commun 36(10):2202–2217

    Article  Google Scholar 

  7. 7.

    Du J, Gelenbe E, Jiang C, Zhang H, Ren Y (2017) Contract design for traffic offloading and resource allocation in heterogeneous ultra-dense networks. IIEEE J Sel Areas Commun 35(11): 2457–2467

  8. 8.

    Anderson T, Peterson L, Shenker S, Turner J (2005) Overcoming the internet impasse through virtualization. Computer 38(4):34–41

    Article  Google Scholar 

  9. 9.

    Tutschku K, Zinner T, Nakao A, Phuoc TG (2009) Network virtualization: Implementation steps towards the future internet. J Hum Behav Soc Environ 22(4):463–478

    Google Scholar 

  10. 10.

    Amaldi E, Coniglio S, Koster AMCA, Tieves M (2016) On the computational complexity of the virtual network embedding problem. Electron Notes Discrete Math 52:213–220

    MathSciNet  Article  Google Scholar 

  11. 11.

    Yong Z, Ammar MH (2006) Algorithms for assigning substrate network resources to virtual network components. In: Infocom IEEE international conference on computer communications

  12. 12.

    Diallo M, Quintero A, Pierre S (2019) An efficient approach based on ant colony optimization and tabu search for a resource embedding across multiple cloud providers. IEEE Trans Cloud Comput: 1–1

  13. 13.

    Cao H, Han H, Qu Z, Yang L (2018) Heuristic solutions of virtual network embedding: A survey. China Commun 15(3):186–214

    Article  Google Scholar 

  14. 14.

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

  15. 15.

    Chowdhury NMMK, Rahman MR, Boutaba R (2009) Virtual network embedding with coordinated node and link mapping. In: Infocom

  16. 16.

    Gao X, Yu H, Anand V, Gang S, Hao D (2010) A new algorithm with coordinated node and link mapping for virtual network embedding based on lp relaxation. In: Asia communications & photonics conference & exhibition, pp 152–153

  17. 17.

    Cao H, Wu S, Aujla G, Wang Q, Yang L, Zhu H (2019) Dynamic embedding and quality of service driven adjustment for cloud networks. IEEE Trans Industr Inform 16(2):1406–1416

    Article  Google Scholar 

  18. 18.

    Cao H, Wu S, Hu Y, Mann R, Liu Y, Yang L, Zhu H (2019) An efficient energy cost and mapping revenue strategy for inter-domain nfv-enabled networks. IEEE Internet of Things Journal: 1–1

  19. 19.

    Cao H, Zhu Y, Zheng G, Yang L (2018) A novel optimal mapping algorithm with less computational complexity for virtual network embedding. IEEE Trans Netw Serv Manag 15(1):356–371

    Article  Google Scholar 

  20. 20.

    Cao H, Yang L, Zhu H (2018) Novel node-ranking approach and multiple topology attributes-based embedding algorithm for single-domain virtual network embedding. IEEE Internet Things J 5(1):108–120

    Article  Google Scholar 

  21. 21.

    Zhang Z, Xiang C, Su S, Wang Y, Yan L (2013) A unified enhanced particle swarm optimization-based virtual network embedding algorithm. Int J Commun Syst 26(8):1054–1073

    Article  Google Scholar 

  22. 22.

    Li W, Hua Q, Zhao J, Guo Y (2014) Virtual network embedding with discrete particle swarm optimisation. Electron Lett 50(4):285–286

    Article  Google Scholar 

  23. 23.

    Mi X, Chang X, Liu J, Sun L, Xing B (2012) Embedding virtual infrastructure based on genetic algorithm

  24. 24.

    Pathak I, Vidyarthi DP (2017) A model for virtual network embedding across multiple infrastructure providers using genetic algorithm. Sci China Inf Sci 60(4):040308

    Article  Google Scholar 

  25. 25.

    Zhuang L, Wang G, Wang M, Zhang K (2018) A virtual network embedding algorithm based on cellular automata genetic mechanism. MATEC Web of Conferences 232(4):01019

    Article  Google Scholar 

  26. 26.

    Jiang C, Chen Y, Liu KJR, Ren Y (2013) Renewal-theoretical dynamic spectrum access in cognitive radio network with unknown primary behavior. IEEE J Sel Areas Commun 31(3):406–416

    Article  Google Scholar 

  27. 27.

    Cai J, Nian X, Gu H, Zhang L (2013) A user priority-based virtual network embedding model and its implementation. In: IEEE International conference on electronics information & emergency communication

  28. 28.

    Zhou B, Wen G, Zhao S, Lu X, Zhong D, Wu C, Qiang Y (2014) Virtual network mapping for multi-domain data plane in software-defined networks. In: International conference on wireless communications

  29. 29.

    Jiang C, Chen Y, Gao Y, Liu KJR (2013) Joint spectrum sensing and access evolutionary game in cognitive radio networks. IEEE Trans Wirel Commun 12(5):2470–2483

    Article  Google Scholar 

  30. 30.

    Dorigo M, Blum C (2005) Ant colony optimization theory: A survey. Theor Comput Sci 344 (2-3):243–278

    MathSciNet  Article  Google Scholar 

  31. 31.

    Shang G, Jiang X, Tang K (2007) Hybrid algorithm combining ant colony optimization algorithm with genetic algorithm. In: 2007 Chinese control conference, pp 701–704

  32. 32.

    Lee MG, Yu KM (2018) Dynamic path planning based on an improved ant colony optimization with genetic algorithm. In: 2018 IEEE Asia-Pacific conference on antennas and propagation (APCAP), pp 1–2

  33. 33.

    Wei YI, Wang JW, Pan HB, Li LI (2011) Ant colony chaos genetic algorithm for mapping task graphs to a network on chip. Acta Electronica Sinica 39(8):1832–1836

    Google Scholar 

  34. 34.

    Zegura EW, Calvert KL, Bhattacharjee S (1996) How to model an internetwork. IEEE Infocom 2:594–602

    Google Scholar 

  35. 35.

    Jiang C, Zhang H, Ren Y, Han Z, Chen K, Hanzo L (2017) Machine learning paradigms for next-generation wireless networks. IEEE Wirel Commun 24(2):98–105

    Article  Google Scholar 

  36. 36.

    Yao H, Chen X, Li M, Zhang P, Wang L (2018) A novel reinforcement learning algorithm for virtual network embedding. Neurocomputing 284:1–9

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the National Key Research and Development Program of China under Grant 2020YFB1804800, partially supported by the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-006, and partially supported by Shandong Provincial Natural Science Foundation under Grant ZR2020MF006. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Chunxiao Jiang.

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

Verify currency and authenticity via CrossMark

Cite this article

Zhang, P., Liu, F., Jiang, C. et al. A Multi-Domain VNE Algorithm Based on Load Balancing in the IoT Networks. Mobile Netw Appl (2021). https://doi.org/10.1007/s11036-020-01714-0

Download citation

Keywords

  • Internet of Things
  • Virtual network mapping
  • Genetic algorithm
  • Ant colony algorithm
  • Load balancing