Energy-Aware and Location-Constrained Virtual Network Embedding in Enterprise Network

  • Xin Cong
  • Lingling ZiEmail author
  • Kai Shuang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)


Network virtualization can integrate the servers and computers from different locations in the large enterprises. Most of prior studies on the network virtualization execute on the cloud platform and they are not suit for the enterprise network. Therefore, the problem of the energy-aware and location-constrained virtual network embedding (EL-VNE) in the enterprise network is proposed and solved in this paper. Firstly, both the computing capability and bandwidth capability are unified by adopting the complex number theory and their corresponding capabilities of nodes, including physical and virtual nodes are determined. Then EL-VNE model is presented and proved to be a NP-complete problem, so as to make the virtual network embedding process only need node mapping without link mapping. Finally, a heuristic algorithm is presented to minimize the energy consumption on the condition of location constraint of nodes. The experiments show that the proposed EL-VNE can get less energy consumption compared with EAD, and simultaneously have better performance compared with GLC.


Energy aware Location constraint VNE Enterprise network 



This work is partially supported by The National Natural Science Foundation of China (No. 61602227, 61702241); The Foundation of the Education Department of Liaoning Province (No. LJYL019); The Doctoral Starting up Foundation of Science Project of Liaoning Province (No. 201601365); National Key Research and Development Program of China(No. 2016QY01W0200); The Foundation of Liaoning Educational Committee (No. LJYL019).


  1. 1.
    Ogino, N., Kitahara, T., Arakawa, S.: Virtual network embedding with multiple priority classes sharing substrate resources. Comput. Netw. 112, 52–66 (2017)CrossRefGoogle Scholar
  2. 2.
    Chochlidakis, G., Friderikos, V.: Mobility aware virtual network embedding. IEEE Trans. Mob. Comput. 16(5), 1343–1356 (2017)CrossRefGoogle Scholar
  3. 3.
    Cao, Z., Lin, J.: Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans. Smart Grid 8(4), 1943–1955 (2017)MathSciNetGoogle Scholar
  4. 4.
    Yousafzai, A., Gani, A., Noor, R.M.: Cloud resource allocation schemes: review, taxonomy, and opportunities. Knowl. Inf. Syst. 50(2), 347–381 (2017)CrossRefGoogle Scholar
  5. 5.
    Papagianni, C., Leivadeas, A., Papavassiliou, S.: On the optimal allocation of virtual resources in cloud computing networks. IEEE Trans. Comput. 62(6), 1060–1071 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hong, H.J., Chen, D.Y., Huang, C.Y.: Placing virtual machines to optimize cloud gaming experience. IEEE Trans. Cloud Comput. 3(1), 42–53 (2015)CrossRefGoogle Scholar
  7. 7.
    Fard, S.Y.Z., Ahmadi, M.R., Adabi, S.: A dynamic VM consolidation technique for QoS and energy consumption in cloud environment. J. Supercomput. 73(10), 4347–4368 (2017)CrossRefGoogle Scholar
  8. 8.
    Esfandiarpoor, S., Pahlavan, A., Goudarzi, M.: Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing. Comput. Electr. Eng. 42, 74–89 (2015)CrossRefGoogle Scholar
  9. 9.
    Elijorde, F., Lee, J.: Attaining reliability and energy efficiency in cloud data centers through workload profiling and SLA-aware VM assignment. Int. J. Soft Comput. Appl. 7(1), 41–58 (2015)Google Scholar
  10. 10.
    Wu, J., Zhang, Y., Zukerman, M.: Energy-efficient base-stations sleep-mode techniques in green cellular networks: a survey. IEEE Commun. Surv. Tutor. 17(2), 803–826 (2015)CrossRefGoogle Scholar
  11. 11.
    Sun, G., Yu, H., Li, L.: Exploring online virtual networks mapping with stochastic bandwidth demand in multi-datacenter. Photonic Netw. Commun. 23(2), 109–122 (2012)CrossRefGoogle Scholar
  12. 12.
    Chowdhury, M., Rahman, M.R., Boutaba, R.: Vineyard: virtual network embedding algorithms with coordinated node and link mapping. IEEE/ACM Trans. Netw. (TON) 20(1), 206–219 (2012)CrossRefGoogle Scholar
  13. 13.
    Gong, L., Jiang, H., Wang, Y.: Novel location-constrained virtual network embedding LC-VNE algorithms towards integrated node and link mapping. IEEE/ACM Trans. Netw. 24(6), 3648–3661 (2016)CrossRefGoogle Scholar
  14. 14.
    Zhang, Z., Su, S., Zhang, J.: Energy aware virtual network embedding with dynamic demands: online and offline. Comput. Netw. 93, 448–459 (2015)CrossRefGoogle Scholar
  15. 15.
    Wang, M., Meng, X., Zhang, L.: Consolidating virtual machines with dynamic bandwidth demand in data centers. In: INFOCOM, 2011 Proceedings IEEE, pp. 71–75 (2011)Google Scholar
  16. 16.
    Garey, M.R., Johnson, D.S.: Computers and Intractablity: A Guide to the Theory of NP-Completeness. Freeman, San Francisco (1990). 210, problem ND17Google Scholar
  17. 17.
    Zegura, E.W., Calvert, K.L., Bhattacharjee, S.: How to model an internetwork. In: INFOCOM, Proceedings IEEE, vol. 2, pp. 594–602. IEEE (1996)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringLiaoning Technical UniversityHuludaoChina
  2. 2.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations