ABSTRACT
Abstract
Current MANO solutions and existing tools for network slicing and service orchestration are still implemented as silo-based control and orchestration tools, mostly addressing the coordination of monolithic pipelined services that cannot be easily and transparently adapted to dynamic NG-IoT network and service conditions. Lack of agility and flexibility in the service and slice lifecycle management, as well as in the runtime operation, is indeed still an evident limitation. A tight integration of AI/ML techniques can help in augmenting the slice and service orchestration logics automation and intelligence, as well as their self-adaptation capabilities to satisfy NG-IoT service dynamics.
