The Energy-Efficient and Edge-Aware Network Slicing with Machine Learning (NSML) project proposes developing models for resource orchestration in network slicing. The resource orchestration focuses on energy efficiency and edge computing in an Internet of Things (IoT) scenario with Publish/Subscribe communication.

The NSML project is aligned with the architecture of the “Slicing Future Internet Architectures” (SFI2) project. The SFI2 architecture proposes advances concerning network slicing with the use of machine learning native to the architecture (ML-native architecture), allocation of resources with energy efficiency (energy-efficient), and security-oriented network slicing (slice-security tailored). The native machine learning in NSML slicing allows slicing with multiple objectives on a large scale and with heterogeneous computational requirements.