Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

TL;DR

The paper addresses the need for intelligent orchestration of architectural features in network slicing to handle dynamic NGNM environments. It proposes ML-native agents, federated learning, and a data-driven management approach within the SFI2 reference architecture, and demonstrates a security-focused case that uses distributed ML and federated averaging. The key contributions include defining architectural features, outlining position points for ML-enabled orchestration, detailing a data-pipeline for monitoring and analytics, and validating a federated, distributed ML security case in an industrial setting. The work highlights the practical impact of embedding intelligent, distributed learning within NS architectures to improve resource allocation, security, and cross-domain orchestration across multi-domain NS deployments.

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.

Figures (5)

• Figure 1: The SFI2 (Slicing Future Internet Infrastructures) Network Slicing Reference Architecture.
• Figure 2: ML-Native Agents and Federated Learning Recommendations for Architectural Features Orchestration.
• Figure 3: Architectural Data-Driven Recommendation for Network Slicing.
• Figure 4: Frequency of the most important features in whole Westermo dataset.
• Figure 5: Localized Test Accuracy (%) achieved by three different ML-Agents in the SFI2 Slicing Architecture.