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 should solve some of the problems of future network deployment and operation.

Our Roles & Activities

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.


A full list of related standards in the public domain is accessible via the ETSI standards search.


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

The direct link to refer to this blog is https://www.etsi.org/newsroom/blogs/blog-eni


ENI#8 was held on 3-5 December in 5Tonic labs in Madrid, hosted by UC3M (University Carlos III of Madrid) and Telefonica: the host gave a presentation of the 5tonic laboratory. Attendees included members and participants of the group including Government Ministry Institutes. It was a fruitful meeting with over 80 documents handled.

This meeting was important to discuss the ISG ENI extension terms of reference for Release 2 going into its second term. The group updated the Terms of Reference accordingly and will ask ETSI’s Director General for an extension from April 2019 to March 2021, based on the growing ongoing and future work of the group.

ENI8 blog 18

Expected and Future work of ETSI ISG ENI

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ETSI ISG ENI Chairman, Aria’s Head of Research and ISG ENI Technical manager Outline ETSI ENI’s AI Use Cases, System Architecture and the China Telecom Led Proof of concept at SDN NFV World Congress in The Hague.

Over the last few years, Layer123’s SDN NFV World Congress has emerged as the best place to assess the mood and state of progressive thinking in telecom operations. So it was fitting that this year’s program included a progress report from the Experiential Networked Intelligence (ENI) Industry Specification Group (ISG) team developing a reference model for the use of AI in telecom operations.


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