This activity aims to identify the basic network slicing strategies, their requirements, and dependencies concerning the communication resource allocation functionality for the orchestration, implementation, and management. Slice elasticity and network admission control concerning communication resources are focal points of this activity.
This activity aims at the development of machine learning-based knowledge acquisition modules for the DyRA framework. The activity includes modeling, developing algorithms, and simulating the learning associated with the strategy and mechanism of orchestration and allocation of network slice communication resources.
This activity aims to implement the DyRA framework, embed the modules and function blocks necessary to deploy use cases.
This activity aims at prototyping network slicing cases in specific application contexts. The activity aims to apply and validate the cognition modules with the functional requirements of the application scenario.
This permanent activity corresponds to the dissemination of the results of the NSRAlloc-ML Project through workshops, lectures, and institutional articulations.