TECHNICAL DEVELOPMENT STRATEGY AND METHODOLOGY
OBJECTIVES
01
Distributed management plane to support massive deployment of network slices:
Devise a distributed management plane to handle the deployment of a massive number of network slices.
02
Definition of novel end-to-end (e2e) slice Key Performance Indicators (KPIs) and development of AI-based mechanisms for their accurate prediction from multi-level metrics:
Define network slice service-level KPIs that consider not only a single VNF, but all the network slice components, i.e., VNFs, PNFs and networking components.
03
Data-driven management system based on federated learning:
Devise data-driven management system components (MS, AE, DE), based on SoA federated learning AI techniques.
04
Zero touch network configuration:
Combine the Intent-based policy definition and the cognitive management entities, to target multi-domain zero-touch network configuration of sliced B5G networks.
05
DE decisions tailored to the RAN:
Define decision algorithms tailored to the RAN. The envisioned decisions should allow the update of the RAN configuration when necessary, to meet the heterogeneous performance requirements of multiple coexisting slices.
06
AI-driven slice security management via robust and efficient trust-based mechanisms:
Elaborate and advanced AI-driven security mechanisms for automatic identification and mitigation of both in-slice and cross-slice attacks.
07
AI-driven energy efficient network management:
Provide ML-assisted techniques to optimize energy efficiency in all technological domains (i.e., Cloud, RAN, Core and MEC).
08
Dissemination, standardization and exploitation of MonB5G:
Dissemination, standardisation and exploitation of technologies developed in the MonB5G project. Special focus is given to push the solutions regarding the cognitive AE and DE to ETSI ZSM and ENI.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 871780
