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Machine Learning-Assisted Closed-Control Loops for Beyond 5G Multi-Domain Zero-Touch Networks

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Abstract

End-to-End (E2E) services in beyond 5G (B5G) networks are expected to be built upon resources and services distributed in multi-domain, multi-technology environments. In such scenarios, key challenges around multi-domain management and collaboration need to be tackled. ETSI Zero-touch network and Service Management (ZSM) architectural framework provides the structure and methods for effectively delivering E2E network services. ZSM pursues cross-domain automation with minimum human intervention through two main enablers: Closed Control Loop (CCL) and Artificial Intelligence (AI). In this work, we propose a multi-domain ZSM-based architecture aiming at B5G scenarios where several per-domain CCLs leverage Machine Learning (ML) methods to collaborate in E2E service management tasks. We instantiate the architecture in the use case scenario of multi-domain automated healing of Dynamic Adaptive Streaming over HTTP (DASH) video services. We present two ML-assisted techniques, first to estimate a Service Level Agreement (SLA) violation through a Edge-based Quality of Experience (QoE) Probe, and second to identify the root cause at the core transport network. Results from the experimental evaluation in an emulation environment using real mobile network traces point to the potential benefits of applying ML techniques for QoS-to-QoE estimation at Multi-Access Edge Computing facilities and correlation to faulty transport network links. Altogether, the work contributes towards a vision of ML-based sandbox environments in the spirit of E2E service and network digital twins towards the realization of automated, multi-domain CCLs for B5G.

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Notes

  1. Alternatively, the migration task could include the video DASH server at its new location DC2 sending HTTP redirect messages to active users among other well-known Service/VM migration techniques

  2. http://skulddata.cs.umass.edu/traces/mmsys/2013/pathbandwidth

  3. https://www.ucc.ie/en/misl/research/datasets/ivid_4g_lte_dataset

  4. https://github.com/uccmisl/5Gdataset

  5. https://scikit-learn.org/stable/

  6. https://scikit-learn.org/stable/modules/generated/sklearn. model_selection.GridSearchCV.html

  7. https://selfnet-5g.eu/

  8. https://5g-ppp.eu/cognet/

  9. https://5gzorro.eu/

  10. https://5growth.eu/

  11. https://inspire-5gplus.eu/

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Acknowledgements

This research was supported by the Innovation Center, Ericsson S.A., Brazil, grant UNI.67. The views expressed are solely those of the authors and do not necessarily represent Ericsson’s official standpoint.

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Authors and Affiliations

  1. UNICAMP, Campinas, Brazil

    Nathan Franklin Saraiva de Sousa, Md Tariqul Islam, Raza Ul Mustafa, Danny Alex Lachos Perez & Christian Esteve Rothenberg

  2. Ericsson Research, São Paulo, Brazil

    Pedro Henrique Gomes

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  1. Nathan Franklin Saraiva de Sousa
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  2. Md Tariqul Islam
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Correspondence to Nathan Franklin Saraiva de Sousa.

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de Sousa, N.F.S., Islam, M.T., Mustafa, R.U. et al. Machine Learning-Assisted Closed-Control Loops for Beyond 5G Multi-Domain Zero-Touch Networks. J Netw Syst Manage 30, 46 (2022). https://doi.org/10.1007/s10922-022-09651-x

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