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Improved Flow Awareness by Intelligent Collaborative Sampling in Software Defined Networks

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5G for Future Wireless Networks (5GWN 2019)

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Abstract

To improve the specific quality of service, internal network management and security analysis in the future mobile network, accurate flow-awareness in the global network through packet sampling has been a viable solution. However, the current traffic measurement method with the five tuples cannot recognize the deep information of flows, and the Deep Packet Inspection (DPI) deployed at the gateways or access points is lack of traffic going through the internal nodes (e.g., base station, edge server). In this paper, by means of Deep Q-Network (DQN) and Software-Defined Networking (SDN) technique, we propose a flow-level sampling framework for edge devices in the Mobile Edge Computing (MEC) system. In the framework, an original learning-based sampling strategy considering the iterative influences of nodes is used for maximizing the long-term sampling accuracy of both mice and elephant flows. We present an approach to effectively collect traffic packets generated from base stations and edge servers in two steps: (1) adaptive node selection, and (2) dynamic sampling duration allocation by Deep Q-Learning. The results show that the approach can improve the sampling accuracy, especially for mice flows.

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Notes

  1. 1.

    http://sguangwang.com/TelecomDataset.html.

  2. 2.

    http://mawi.wide.ad.jp/mawi/.

References

  1. Benson, T., Akella, A., Maltz, D.A.: Network traffic characteristics of data centers in the wild. In: ACM SIGCOMM Conference on Internet Measurement, pp. 267–280, November 2010

    Google Scholar 

  2. Patel, M., et al.: Mobile-edge computing–introductory technical white paper. White Paper, Mobile-Edge Computing (MEC) Industry Initiative (2014)

    Google Scholar 

  3. Bremler-Barr, A., Harchol, Y., Hay, D., Koral, Y.: Deep packet inspection as a service. In: ACM CoNEXT, pp. 271–282, December 2014

    Google Scholar 

  4. Zhu, Y.B., Kang, N.X., Cao, J.X., Greenberg, A., Lu, G.H.: Packet-level telemetry in large datacenter networks. ACM SIGCOMM 45(4), 479–491 (2015)

    Google Scholar 

  5. Su, Z.Y., Wang, T., Hamdi, M.: COSTA: cross-layer optimization for sketch-based software defined measurement task assignment. In: IEEE IWQoS, pp. 183–188, June 2015

    Google Scholar 

  6. Yu, M., Jose, L., Miao, R.: Software defined traffic measurement with OpenSketch. In: USENIX NSDI, pp. 29–42, April 2013

    Google Scholar 

  7. Bouet, M., Leguay, J., Conan, V.: Cost-based placement of virtualized deep packet inspection functions in SDN. In: IEEE MILCOM, pp. 992–997, February 2014

    Google Scholar 

  8. Suh, J., Kwon, T.T., Dixon, C., Felter, W., Carter, J.: OpenSample: a low-latency, sampling-based measurement platform for commodity SDN. In: IEEE ICDCS, pp. 228–237, July 2014

    Google Scholar 

  9. Xing, C.Y., Ding, K., Hu, C., Chen, M.: Sample and fetch-based large flow detection mechanism in software defined networks. IEEE Commun. Lett. 20(9), 1764–1767 (2016)

    Article  Google Scholar 

  10. Yoon, S., Ha, T., Kim, S., Lim, H.: Scalable traffic sampling using centrality measure on software-defined networks. IEEE Commun. Mag. 55, 43–49 (2017)

    Article  Google Scholar 

  11. Ha, T., Kim, S., An, N., Narantuya, J., Jeong, C., Kim, J.W.: Suspicious traffic sampling for intrusion detection in software-defined networks. Comput. Netw. 109(2), 172–182 (2016)

    Article  Google Scholar 

  12. Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018)

    Article  Google Scholar 

  13. Su, Z., Wang, T., Xia, Y., Hamdi, M.: CeMon: a cost-effective flow monitoring system in software defined networks. Comput. Netw. 92(1), 101–115 (2015)

    Article  Google Scholar 

  14. He, Y., Zhao, N., Yin, H.X.: Integrated networking, caching, and computing for connected vehicles: a deep reinforcement learning approach. IEEE Trans. Veh. Technol. 67(1), 44–55 (2018)

    Article  Google Scholar 

  15. Li, H., Gao, H., Lv, T.J., Lu, Y.: Deep Q-learning based dynamic resource allocation for self-powered ultra-dense networks. In: IEEE ICC, pp. 1–6, July 2018

    Google Scholar 

  16. Lu, L., Chen, D., Ren, X.L., Zhang, Q.M., Zhang, Y.C.: Vital nodes identification in complex networks. Phys. Rep. 65, 1–63 (2016)

    MathSciNet  Google Scholar 

  17. Vanini, E., Pan, R., Alizadeh, M., Taheri, P., Edsall, T.: Let it flow resilient asymmetric load balancing with flowlet switching. In: USENIX NSDI, pp. 407–420, May 2017

    Google Scholar 

  18. Van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double Q-learning. In: Proceedings 30th AAAI Conference Artificial Intelligence, pp. 2094–2100 (2016)

    Google Scholar 

  19. Wang, S., Zhao, Y., Xu, J., Yuan, J., Hsu, C.H.: Edge server placement in mobile edge computing, June 2018. https://doi.org/10.1016/j.jpdc.2018.06.008

    Article  Google Scholar 

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Authors and Affiliations

  1. Tianjin Key Laboratory of Advanced Networking, College of Intelligence and Computing, Tianjin University, Tianjin, China

    Jun Deng, He Cai & Xiaofei Wang

Authors
  1. Jun Deng
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  2. He Cai
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  3. Xiaofei Wang
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Corresponding author

Correspondence to Xiaofei Wang .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

    Victor C. M. Leung

  2. University of Science and Technology, Beijing, China

    Haijun Zhang

  3. Shenzhen University, Shenzhen, China

    Xiping Hu

  4. National University of Defense Technology, Changsha, China

    Qiang Liu

  5. Shizuoka University, Shizuoka, Japan

    Zhi Liu

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Deng, J., Cai, H., Wang, X. (2019). Improved Flow Awareness by Intelligent Collaborative Sampling in Software Defined Networks. In: Leung, V., Zhang, H., Hu, X., Liu, Q., Liu, Z. (eds) 5G for Future Wireless Networks. 5GWN 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-17513-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-17513-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17512-2

  • Online ISBN: 978-3-030-17513-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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