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Review

AI-driven Service and Slice Orchestration

G. Bernini, P. Piscione, E. Seder  1
Rute C. Sofia  1 , John Soldatos  2
, editors.
In: Shaping the Future of IoT with Edge Intelligence: How Edge Computing Enables the Next Generation of IoT Applications. Abingdon (UK): River Publishers; 2024 Jan. Chapter 2.
Taylor & Francis Collection of European Union Research and Innovation Funded Monographs and Chapters.
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Review

AI-driven Service and Slice Orchestration

G. Bernini et al.
Free Books & Documents

Excerpt

Current MANO solutions and existing tools for network slicing and service orchestration are still implemented as silo-based control and orchestration tools, mostly addressing the coordination of monolithic pipelined services that cannot be easily and transparently adapted to dynamic NG-IoT network and service conditions. Lack of agility and flexibility in the service and slice lifecycle management, as well as in the runtime operation, is indeed still an evident limitation. A tight integration of AI/ML techniques can help in augmenting the slice and service orchestration logics automation and intelligence, as well as their self-adaptation capabilities to satisfy NG-IoT service dynamics.

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References

    1. H2020 iNGENIOUS, https://ingenious-iot.eu/web/
    1. 3GPP TS 28.500, “Management concepts, architecture and requirements for mobile networks that include virtualized network functions (Release 16f, v 16.0.0, July 2020.
    1. ETSI GS NFV-MAN 001, “Network Function Virtualisation (NFV); Management and Orchestration”, v 1.1.1, December 2014.
    1. 3GPP TS 23.501, “System architecture for the 5G System (5GS); Stage 2 (Release 17)”, vl7.1.1, June 2021.
    1. 3GPP TS 28.530, “Management and Orchestration; Concepts, use cases and requirements (Release 17)”, vl7.1.0, March 2021.

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