Experiential Networked Intelligence

Our Experiential Networked Intelligence Industry Specification Group (ENI ISG) is defining a Cognitive Network Management architecture, using Artificial Intelligence (AI) techniques and context-aware policies to adjust offered services based on changes in user needs, environmental conditions and business goals. 

The use of Artificial Intelligence techniques in the network management system could help solve some of the problems of future network deployment and operation.

Our ISG on Experiential Networked Intelligence (ENI) develops standards for a Cognitive Network Management system with the aim to introduce a metric for the optimization and adjustment of the operator experience over time by taking advantage of machine learning and reasoning. We employ the ‘monitor-analyse-plan-execute’ control model which enables the system to adjust the offered services based on changes in user needs, environmental conditions and business goals.

The policy modelling will encompass open intelligent functionality for network configuration and management. We provide inputs and objectives to support the industry’s progress in intelligent policy-based management.

The introduction of technologies such as SDN, NFV and network slicing means that networks are becoming more flexible and powerful. These technologies transfer much of the complexity in a network from hardware to software, from the network itself to its management and operation. ENI will make the deployment of SDN and NFV more intelligent and efficient and will assist the management and orchestration of the network.

ENI has specified a set of use cases and the derived requirements for a generic technology independent architecture of a network supervisory assistant system based on the ‘observe-orient-decide-act’ control loop model. This model can assist decision-making systems, such as network control and management systems, to adjust services and resources offered based on changes in user needs, environmental conditions and business goals.

A gap analysis of work on context-aware and policy-based standards is being considered, working with other Standards Developing Organizations to reuse existing standardized solutions for legacy and evolving network functions wherever possible. Adding closed-loop AI mechanisms based on context-aware, metadata-driven policies to more quickly recognize and incorporate new and changed knowledge, and hence, make actionable decisions, in day-to-day-operations. Security and a closed loop learning policy-model are subjects to be addressed shortly.

The list of ENI related deliverables in draft stage is available via the ETSI Work Programme on the Portal.

The following is a list of the latest published ETSI standards on Experiential Networked Intelligence:

Standard No. Standard title.
GR ENI 004 Experiential Networked Intelligence (ENI); Terminology for Main Concepts in ENI
GS ENI 006 Experiential Networked Intelligence (ENI); Proof of Concepts Framework
GR ENI 003 Experiential Networked Intelligence (ENI); Context-Aware Policy Management Gap Analysis
GR ENI 001 Experiential Networked Intelligence (ENI); ENI use cases
GS ENI 002 Experiential Networked Intelligence (ENI); ENI requirements

A full list of related standards in the public domain is accessible via the ETSI standards search. Via this interface you can also subscribe for alerts on updates of ETSI standards.

News, comments and opinions from ETSI’s ENI Industry Specification Group

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