An AI-Assisted Framework for Lifecycle Management of Beyond 5G Services

Abstract

Future mobile communication networks aim to offer services and applications in the most flexible, adaptable and cost-effective manner. B5G networks aim at a fully softwarized network architecture, where hardware and software programming is used for the design, implementation, deployment, management, monitoring and maintenance of network equipment/components/services. Artificial Intelligence (AI) and Machine Learning (ML) techniques are steadily being integrated into 5G systems, offering intelligent automation, proactive network management, and resource allocation optimization. In this environment, the role of Management and Orchestration (MANO) is vital to ensure efficient infrastructure utilization and fulfillment of heterogeneous service requirements. Despite the development of various tools and platforms to facilitate MANO in 5G systems, in most cases there is still the need of human intervention and manual input for configuring the 5G elements according to service requirements. In this paper, a MANO framework has been developed, that specifically targets the orchestration operations of 5G networks. The proposed framework focuses on the lifecycle management of the 5G components, in order to achieve an operational environment with minimal human intervention or manual configuration (Zero Touch Networks -ZTN). Within this ecosystem, an Analytics & AI/ML Platform has comprehensive monitoring capabilities and influences decisions across various layers or aspects of the infrastructure. This includes optimizing the allocation and orchestration of both networking and edge/cloud computing virtual resources within the infrastructure.

Keywords

• 5G
• B5G
• 6G
• MANO
• slicing
• NFV