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SCIENCE CHINA Information Sciences, Volume 60, Issue 4: 040302(2017) https://doi.org/10.1007/s11432-016-9031-x

VNF-FG design and VNF placement for 5G mobile networks

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  • ReceivedNov 24, 2016
  • AcceptedJan 11, 2017
  • PublishedMar 6, 2017

Abstract

Network function virtualization (NFV) is envisioned as one of the critical technologies in 5th-Generation (5G) mobile networks. This paper investigates the virtual network function forwarding graph (VNF-FG) design and virtual network function (VNF) placement for 5G mobile networks. We first propose a two-step method composed of flow designing and flow combining for generating VNF-FGs according to network service requests. For mapping VNFs in the generated VNF-FG to physical resources, we then modify the hybrid NFV environment with introducing more types of physical nodes and mapping modes for the sake of completeness and practicality, and formulate the VNF placement optimization problem for achieving lower bandwidth consumption and lower maximum link utilization simultaneously. To resolve this problem, four genetic algorithms are proposed on the basis of the frameworks of two existing algorithms (multiple objective genetic algorithm and improved non-dominated sorting genetic algorithm). Simulation results show that Greedy-NSGA-II achieves the best performance among our four algorithms. Compared with three non-genetic algorithms (random, backtracking mapping and service chains deployment with affiliation-aware), Greedy-NSGA-II reduces 97.04\%, 87.76\% and 88.42\% of the average total bandwidth consumption, respectively, and achieves only 13.81\%, 25.04\% and 25.41\% of the average maximum link utilization, respectively. Moreover, using our VNF-FG design method and Greedy-NSGA-II together can also reduce the total bandwidth consumption remarkably.


Acknowledgment

Acknowledgments

This work was supported in part by National Science Foundation of China (Grant Nos. 61303250, 61302031), Strategic Pilot Project of Chinese Academy of Sciences (Grant No. XDA06010306) and Scientific Research Foundation of the Institute of Information Engineering, Chinese Academy of Sciences (Grant No. Y6Z0011105).


References

[1] NFV ISG. Network function virtualization white paper. SDN & OpenFlow World Congress. 2014. Google Scholar

[2] NFV ETSI ISG. Network functions virtualisation (NFV); Use cases. 2013. Google Scholar

[3] Dong J K, Jin X, Wang H B, et al. Energy-saving virtual machine placement in cloud data centers. In: Proceedings of 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. New York: ACM, 2013. 618--624. Google Scholar

[4] Shi W M, Hong B. Towards profitable virtual machine placement in the data center. In: Proceedings of 4th IEEE International Conference on Utility and Cloud Computing. Piscataway: IEEE, 2011. 138--145. Google Scholar

[5] Pietri I, Sakellariou R. Mapping virtual machines onto physical machines in cloud computing: a survey. ACM Comput Surv, 2016, 49: 49 Google Scholar

[6] Fischer A, Botero J F, Beck M, et al. Virtual network embedding: a survey. IEEE Commun Surv Tutor, 2013, 15: 1888-1906 CrossRef Google Scholar

[7] Ye Z L, Cao X J, Wang J P, et al. Joint topology design and mapping of service function chains for efficient, scalable, and reliable network functions virtualization. IEEE Network, 2016, 30: 81-87 CrossRef Google Scholar

[8] Moens H, Turck F D. VNF-P: a model for efficient placement of virtualized network functions. In: Proceedings of 10th IEEE International Conference on Network and Service Management. Piscataway: IEEE, 2014. 418--423. Google Scholar

[9] Mehraghdam S, Keller M, Karl H. Specifying and placing chains of virtual network functions. In: Proceedings of 3rd IEEE International Conference on Cloud Networking. Piscataway: IEEE, 2014. 7--13. Google Scholar

[10] Mijumbi R, Serrat J, Gorricho J L, et al. Design and evaluation of algorithms for mapping and scheduling of virtual network functions. In: Proceedings of 1st IEEE Conference on Network Softwarization. Piscataway: IEEE, 2015. 1--9. Google Scholar

[11] Sahhaf S, Tavernier W, Rost M, et al. Network service chaining with optimized network function embedding supporting service decompositions. Comput Netw, 2015, 93: 492-505 CrossRef Google Scholar

[12] Clayman S, Maini E, Galis A, et al. The dynamic placement of virtual network functions. In: Proceedings of IEEE Network Operations and Management Symposium. Piscataway: IEEE, 2014. 1--9. Google Scholar

[13] Luizelli M C, Bays L R, Buriol L S, et al. Piecing together the NFV provisioning puzzle: efficient placement and chaining of virtual network functions. In: Proceedings of IFIP/IEEE International Symposium on Integrated Network Management. Piscataway: IEEE, 2015. 98--106. Google Scholar

[14] Mijumbi R, Serrat J, Gorricho J, et al. Network function virtualization: state-of-the-art and research challenges. IEEE Commun Surv Tutor, 2015, 18: 236-262 Google Scholar

[15] Fonseca C M, Fleming P J. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proceedings of 5th International Conference on Genetic Algorithms. San Francisco: Morgan Kaufmann Publishers Inc., 1993. 416--423. Google Scholar

[16] Deb K, Agrawal S, Pratap A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of International Conference on Parallel Problem Solving from Nature. Berlin: Springer-Verlag, 2000. 849--858. Google Scholar

[17] Sun Q Y, Lu P, Lu W, et al. Forecast-assisted NFV service chain deployment based on affiliation-aware vNF placement. In: Proceedings of IEEE Global Communications Conference. Piscataway: IEEE, 2016. 1--6. Google Scholar

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