Published January 10, 2024 | Version v1
Conference paper Open

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Creators

  • 1. Universidade do Minho
  • 2. Universidade de Uberlândia
  • 3. ROR icon Universidade de São Paulo
  • 4. Universidade Salvador (UNIFACS)

Description

 Network slicing is a crucial enabler and a trend for the Next Generation Mobile Network (NGMN) and various other new systems like the Internet of Vehicles (IoV) and Industrial IoT (IIoT). Orchestration and machine learning are key elements with a crucial role in the network-slicing processes since the NS process needs to orchestrate resources and functionalities, and machine learning can potentially optimize the orchestration process. However, existing network-slicing architectures lack the ability to define intelligent approaches to orchestrate features and resources in the slicing process. This paper discusses machine learning-based orchestration of features and capabilities in network slicing architectures. Initially, the slice resource orchestration and allocation in the slicing planning, configuration, commissioning, and operation phases are analyzed. In sequence, we highlight the need for optimized architectural feature orchestration and recommend using ML-embed agents, federated learning intrinsic mechanisms for knowledge acquisition, and a data-driven approach embedded in the network slicing architecture. We further develop an architectural features orchestration case embedded in the SFI2 network slicing architecture. An attack prevention security mechanism is developed for the  SFI2 architecture using distributed embedded and cooperating ML agents. The case presented illustrates the architectural feature's orchestration process and benefits, highlighting its importance for the network slicing process. 

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Additional details

Related works

Continues
Conference proceeding: 10.36227/techrxiv.21746285.v1 (DOI)
Is derived from
Journal article: 10.1109/ACCESS.2023.3292788 (DOI)
Preprint: 10.5281/zenodo.7833487 (DOI)
Journal article: 10.1109/TNSM.2023.3287651 (DOI)
Is variant form of
Conference paper: 10.1007/978-3-031-28451-9_11 (DOI)

Dates

Accepted
2024-01-10

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