A Single-Hop Selection Strategy of VNFs Based on Traffic Classification in NFV
Network Function Virtualization (NFV) has become a hot technology since it provides the flexible management of network functions and efficient sharing of network resources. Network resources in NVF require an appropriate management strategy which often manifests as a difficult online decision making task. Resource management in NFV can be thought of as a process of virtualized network functions (VNFs) selection or deployment. This paper proposes a single-hop VNFs selection strategy to realize network resource management. For satisfying quality requirements of different network services, this strategy is based on the results of traffic classification which utilizes Multi-Grained Cascade Forest (gcForest) to distinguish user behaviors on the internet. In the order of VNFs, a network is divided into several layers where each arrived packet needs to queue. The scheduler of each layer selects a layer which hosts the next VNF for the packets in the queue. Experiments prove that the proposed traffic classification method increases the precision by 7.7% and improves the real-time performance. The model of VNFs selection reduces network congestion compared to traditional single-hop scheduling models. Moreover, the number of packets which fail to reach target node in time drops 30% to 50% using the proposed strategy compared to the strategy without the section of traffic classification.
KeywordsNFV Traffic classification Resource management VNFs selection
This work was jointly supported by: (1) National Natural Science Foundation of China (No. 61771068, 61671079, 61471063, 61372120, 61421061); (2) Beijing Municipal Natural Science Foundation (No. 4182041, 4152039); (3) the National Basic Research Program of China (No. 2013CB329102).
- 1.Sun, C., Bi, J., Zheng, Z., et al.: NFP: enabling network function parallelism in NFV. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, pp. 43–56. ACM (2017)Google Scholar
- 3.Chantre, H.D., da Fonseca, N.L.S.: Redundant placement of virtualized network functions for LTE evolved Multimedia Broadcast Multicast Services. In: 2017 IEEE International Conference on Communications, ICC 2017, pp. 1–7. IEEE (2017)Google Scholar
- 8.Shi, H., Li, H., Zhang, D., et al.: Efficient and robust feature extraction and selection for traffic classification. Comput. Netw. Int. J. Comput. Telecommun. Netw. 119(C), 1–16 (2017)Google Scholar
- 9.Zhou, Z.H., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: International Joint Conference on Artificial Intelligence, pp. 3553–3559 (2017)Google Scholar
- 10.Lashkari, A.H., Gil, G.D., Mamun, M.S.I., et al.: Characterization of tor traffic using time based features. In: International Conference on Information Systems Security and Privacy, pp. 253–262 (2017)Google Scholar
- 11.Anderson, B., Mcgrew, D.: Machine learning for encrypted malware traffic classification: accounting for noisy labels and non-stationarity. In: The ACM SIGKDD International Conference, pp. 1723–1732. ACM (2017)Google Scholar
- 14.Joe, I., Kim, W.T., Hong, S.: A network selection algorithm considering power consumption in hybrid wireless networks. IEICE Trans. Commun. 91(1), 1240–1243 (2007)Google Scholar
- 19.Jia, M., Liang, W., Huang, M., et al.: Throughput maximization of NFV-enabled unicasting in software-defined networks. In: 2017 IEEE Global Communications Conference, GLOBECOM 2017, pp. 1–6. IEEE (2017)Google Scholar
- 21.Kar, B., Wu, H.K., Lin, Y.D.: Energy cost optimization in dynamic placement of virtualized network function chains. IEEE Trans. Netw. Serv. Manag. PP(99), 1 (2017)Google Scholar
- 22.Gu, L., Tao, S., Zeng, D., et al.: Communication cost efficient virtualized network function placement for big data processing. In: Computer Communications Workshops, pp. 604–609. IEEE (2016)Google Scholar
- 24.Lee, J., et al.: A real-time message scheduling scheme based on optimal earliest deadline first policy for dual channel wireless networks. In: Sha, E., Han, S.-K., Xu, C.-Z., Kim, M.-H., Yang, L.T., Xiao, B. (eds.) EUC 2006. LNCS, vol. 4096, pp. 264–273. Springer, Heidelberg (2006). https://doi.org/10.1007/11802167_28CrossRefGoogle Scholar
- 25.Li, H., Shenoy, P., Ramamritham, K.: Scheduling messages with deadlines in multi-hop real-time sensor networks. In: IEEE Real Time on Embedded Technology and Applications Symposium. pp. 415–425. IEEE Computer Society (2005)Google Scholar