Autonomic network engineering for the self-managing Future Internet (AFI); Generic Autonomic Network Architecture; Part 2: An Architectural Reference Model for Autonomic Networking@ Cognitive Networking and Self-Management (V1.1.1)
The scope of the present document is to provide the definition of the Generic Autonomic Network Architecture (GANA) as an architectural reference model for autonomic networking@ cognitive networking and self-management that addresses the requirements defined in ETSI TS 103 194 [1] - a compilation of example requirements which reflect realworld problems that benefit from the application of automated management@ autonomic management and selfmanagement principles for networks and services delivered by the network to applications. The objective of the present document is to describe the GANA reference model with its associated Functional Blocks (FBs) and their associated reference points that can be instantiated onto target currently existing@ emerging or future reference network architectures (including their management and control architectures) to create autonomics-enabled reference network architectures and their associated management and control architectures. The present document builds on the ETSI GS AFI 002 [2] specification by extracting key concepts of the GANA model and adding additional aspects that were not covered in ETSI GS AFI 002 [2] and also providing pointers on where to find details on the integration of the GANA model with reference models for other emerging complementary networking paradigms other than autonomics@ namely: ? SON (Self-Organizing Networks). ? SDN (Software-Defined Networking). ? NFV (Network Functions Virtualisation). ? E2E Orchestration. ? Network Analytics. ? Big-Data Analytics for Autonomic Management and Control (AMC) of networks and services; and ? Closed-Loop Service Assurance. This means is recommended that the present document is used together with ETSI GS AFI 002 [2]@ which contains valuable complementary details. The other goal is to describe how the human network operator could govern end to end autonomic networks and their management and control architectures.