Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

TL;DR

This paper explores machine learning-based methods for intelligent orchestration of features and resources in network slicing architectures, emphasizing security and efficiency improvements for next-generation networks.

Contribution

It introduces a data-driven, ML-embedded architectural features orchestration approach within the SFI2 network slicing architecture, including security mechanisms using distributed ML agents.

Findings

Enhanced resource orchestration in network slicing

Security mechanisms using distributed ML agents

Improved efficiency through federated learning

Abstract

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.