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AI-driven predictive and scalable management and orchestration of network slices
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Authors: Slawomir Kuklinski, Lechoslaw Tomaszewski, Robert Kolakowski, Anne-Marie Bosneag, Ashima Chawla, Adlen Ksentini, Sabra Ben Saad, Xu Zhao, Luis A. Garrido, Anestis Dalgkitsis, Bahador Bakhshi, Engin Zeydan
Status: Final
Date of publication: 16 November 2022
Published in: ITU Journal on Future and Evolving Technologies, Volume 3 (2022), Issue 3, Pages 570-588
Article DOI : https://doi.org/10.52953/IPUI5221
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Abstract:
The future network slicing enabled mobile ecosystem is expected to support a wide set of heterogenous vertical services over a common infrastructure. The service robustness and their intrinsic requirements, together with the heterogeneity of mobile infrastructure and resources in both the technological and the spatial domain, significantly increase the complexity and create new challenges regarding network management and orchestration. High degree of automation, flexibility and programmability are becoming the fundamental architectural features to enable seamless support for the modern telco-based services. In this paper, we present a novel management and orchestration platform for network slices, which has been devised by the Horizon 2020 MonB5G project. The proposed framework is a highly scalable solution for network slicing management and orchestration that implements a distributed and programmable AI-driven management architecture. The cognitive capabilities are provided at different levels of management hierarchy by adopting necessary data abstractions. Moreover, the framework leverages intent-based operations to improve its modularity and genericity. The mentioned features enhance the management automation, making the architecture a significant step towards self-managed network slices. |
Keywords: 5G, 6G, AI, management, ML, network slicing, orchestration, ZSM
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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