Intelligent Data-Driven Architectural Features Orchestration for Network
  Slicing
Rodrigo Moreira Flavio de Oliveira Silva Tereza Cristina Melo de Brito Carvalho Joberto S. B. Martins
Network slicing is a crucial enabler and a trend for the Next Generation
Mobile Network (NGMN) and various other new systems like the Internet of
Vehicles (IoV) and Industrial IoT (IIoT). Orchestration and machine learning
are key elements with a crucial role in the network-slicing processes since the
NS process needs to orchestrate resources and functionalities, and machine
learning can potentially optimize the orchestration process. However, existing
network-slicing architectures lack the ability to define intelligent approaches
to orchestrate features and resources in the slicing process. This paper
discusses machine learning-based orchestration of features and capabilities in
network slicing architectures. Initially, the slice resource orchestration and
allocation in the slicing planning, configuration, commissioning, and operation
phases are analyzed. In sequence, we highlight the need for optimized
architectural feature orchestration and recommend using ML-embed agents,
federated learning intrinsic mechanisms for knowledge acquisition, and a
data-driven approach embedded in the network slicing architecture. We further
develop an architectural features orchestration case embedded in the SFI2
network slicing architecture. An attack prevention security mechanism is
developed for the SFI2 architecture using distributed embedded and cooperating
ML agents. The case presented illustrates the architectural feature's
orchestration process and benefits, highlighting its importance for the network
slicing process.