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Review

Intelligent Management at the Edge

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 7.
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Review

Intelligent Management at the Edge

Mohammadreza Mosahebfard et al.
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Excerpt

AI/ML techniques play a key role in 5G/6G networks providing connectivity to IoT devices. In such scenarios, not only is it necessary to run time-sensitive applications with strict latency requirements without human intervention, but it is also key to apply automation techniques at both the application and the network levels. The chapter is composed of three sections. In the first section, we present different cloud native (CN) technologies enabling scalable, costefficient, and reliable IoT solutions. The second section details different distributed and hierarchical monitoring frameworks and metrics collection schemes as inputs to AI engines. In the last section, application placement problems focused on delay minimization in geographically distributed singlecluster environments arc first discussed. Afterwards, application placement issues ensuring latency requirements for the applications and energy consumption in distributed multi-access edge computing (MEC) systems using AI pipelines arc presented.

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References

    1. 5GPPP , A Pre-Structuring Proposal Based on the H2020 Work Programme. [Online]. Available: https://5g-ppp.eu/coverage-plans/
    1. 3GPP , System Architecture for the 5G System (5GS), V. 17.1.1, 3GPP TS 23.501,2021.
    1. 3GPP , Study on management aspects of edge computing, VI 6.0.1, 3GPP TR 28.803,2019.
    1. 3GPP , 5G System Enhancements for Edge Computing, Vl.0.0,, 3GPP TS 23.54, 2021.
    1. 5G ; Self-Organizing Networks (SON) for 5G networks (3GPP TS 28.313 version 16.0.0 Release 16), 2020.

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