sfi2 use case 1
SLICING orchestration using Machine Learning
MACHINE LEARNING IN THE SLICING LIFE-CYCLE
MACHINE LEARNING IN THE SLICING LIFE-CYCLE
The objective of Use Case 1 (UC1) is to use machine learning to orchestrate network slicing. The SFI2 architecture, derived from the NECOS project architecture, supports the Use Case deployment. Architectural components must be explicit about embedding machine learning (ML) techniques and methods in the SFI2 architecture. In this context, the proposition is to extend the NECOS architecture to explicitly deal with machine learning techniques for slice orchestration and other phases of the network slicing life-cycle.
UC1 setup:
UC1 setup:
- Backbone: FIBRE Backbone
- Docker/Kubernets based
- Monitoring: CASANDRA/Prometheus
- Pseudo-Domains: FIBRE islands: UFU, UFG, UFPA, USP
UC1 requirements:
UC1 requirements:
- Pseudo-multidomain slicing
- Multi-service slicing
- QoS/QoE slicing with SLA management
- Multi-objective ML-based optimization
- Distributed traffic generation
UC1 TEAM
UC1 TEAM
UFU: Rafael Pasquini and Flávio Silva
UFPA: Antonio Abelém and Jaime A. Junior
UFV: Rodrigo Moreira
UNIFACS: Joberto Martins and Eduardo Xavier