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Adaptive Telemetry for Software-Defined Mobile Networks

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Abstract

The forthcoming set of 5G standards will bring programmability and flexibility to levels never seen before. This has required introducing changes in the architecture of mobile networks, enabling different features such as the split of control and data planes, as required to support the rapid programming of heterogeneous data planes. Software Defined Networking (SDN) has emerged as a basic toolset for operators to manage their infrastructure, as it opens up the possibility of running a multitude of intelligent and advanced applications for network optimization purposes in a centralized network controller. However, the very basic nature that makes possible this efficient management and operation in a flexible way—the logical centralization—poses important challenges due to the lack of proper monitoring tools, suited for SDN-based architectures. In order to take timely and right decisions while operating a network, centralized intelligence applications need to be fed with a continuous stream of up-to-date network statistics. However, this is not feasible with current SDN solutions due to scalability and accuracy issues. This article first analyzes the monitoring issues in current SDN solutions and then proposes a telemetry framework for software defined mobile networks capable of adapting to the various 5G services. Finally, it presents an experimental validation that shows the benefits of the proposed solution at alleviating the load on the control and data planes, improving the reactiveness to network events, and providing better accuracy for network measurements.

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Notes

  1. Section 2 could be skipped by the expert readers.

  2. Note that there is no standard protocol defined for the northbound interface.

  3. This paper uses as a baseline the latest OpenFlow version, now at 1.5.1 [28].

  4. From here the term stateless.

  5. This paper focuses on the 3 highest transmission rates, which result in an inter-message interval of 3.3 ms, 10 ms, and 100 ms, respectively.

  6. The survival time is the time that an application consuming a communication service may continue to operate without receiving any messages.

  7. An LTM with a TTL equal to 0 is discarded by the network. An LTM message with \(TTL=n\) serves at determining the nth hop.

  8. OpenFlow does not support CFM headers, thus the TTL cannot be decreased by the switch itself as e.g. in the IP protocol.

  9. Accuracy issues are not present because packets are not timestamped.

  10. For instance, the NoviSwitch 21100 is equipped with an Intel Core i7 and the Advantech ESP-9230 with an Intel Xeon.

  11. The clocks of two switches are assumed to be synchronized. See Sect. 5.4 for additional considerations on clock synchronization.

  12. These operations are standard OpenFlow operations and they not involve the ATS API.

  13. https://linuxcontainers.org/lxd/introduction/.

  14. https://www.openvswitch.org/.

  15. https://osrg.github.io/ryu/.

  16. https://github.com/jbeard4/SCION-CORE.

  17. https://nodejs.org/.

  18. https://github.com/node-pcap/node_pcap.

  19. https://github.com/Krb686/nanotimer.

  20. http://tldp.org/HOWTO/Traffic-Control-HOWTO/intro.html.

  21. https://www.netcraftsmen.com/tcpip-performance-factors/.

  22. https://iperf.fr/.

  23. Iperf measurements are based on UDP or TCP sessions.

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Acknowledgements

This work has been partially funded by the H2020 Framework Programme Europe/Taiwan joint action 5G-DIVE Project (Grant No. 859881), by the H2020 Framework Programme EU 5G-Transformer Project (Grant No. 761586), and by the H2020 Framework Programme EU 5Growth Project (Grant No. 856709).

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Authors and Affiliations

  1. ADLINK Technology, Saint-Aubin, France

    Luca Cominardi

  2. University Carlos III of Madrid, Madrid, Spain

    Sergio Gonzalez-Diaz, Antonio de la Oliva & Carlos J. Bernardos

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  1. Luca Cominardi
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  2. Sergio Gonzalez-Diaz
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  3. Antonio de la Oliva
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  4. Carlos J. Bernardos
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Correspondence to Luca Cominardi.

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Cominardi, L., Gonzalez-Diaz, S., de la Oliva, A. et al. Adaptive Telemetry for Software-Defined Mobile Networks. J Netw Syst Manage 28, 660–692 (2020). https://doi.org/10.1007/s10922-020-09524-1

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