ETSI has now completed Release 2 of its Experiential Networked Intelligence (ENI) specifications with the ETSI GS ENI 005 system architecture.
ETSI GS ENI 005 and associated documents will provide better insight into network operations – allowing more effective closed-loop decision making plus better lifecycle management. Through its use, network operators will be able to leverage acquired data and apply artificial Intelligence (AI) algorithms to it. This will mean that operators will be able to respond much quicker to changing situations and gain far greater agility.
The services being delivered across their networks may thereby be rapidly adapted and the resources they have available correctly assigned in accordance with subscribers’ requirements, or any other alterations in circumstances (either operationally or commercially driven).
An industry specification group (ISG) focused on ENI was first established by ETSI five years ago. The organisations involved include many of the leading mobile operators worldwide – full list available here.
ETSI ENI Release 2 defines the key architectural requirements (GS ENI 002), and provides a more comprehensive array of potential use cases (GS ENI 001) and applicable proof of concept (PoC) specifications. To support this release, the ENI ISG has produced a series of reports. These cover data characterization, fault identification, the different types of AI mechanisms that can be employed (along with details of the associated bias and ethical implications), the constituent functional blocks needed for modular design implementations and how to assure their interoperability.
“ENI will have an important part to play in how next generation networks are managed, making them contextually aware and giving them greater inherent flexibility,” states Dr Raymond Forbes, Chair of the ETSI ENI ISG. “Use cases and PoCs are already enabling its validity to be demonstrated, and with ETSI ENI Release 2 we are providing a framework upon which operators and their technology partners can implement it into their infrastructure.”
ETSI’s ENI Release 2 includes the following specifications and reports:
- GS ENI 001 Use Cases
- GS ENI 002 Requirements
- GR ENI 004 Terminology
- GS ENI 005 System Architecture
- GR ENI 008 Intent Aware Network Autonomicity
- GR ENI 009 Data Mechanisms
- GR ENI 010 Evaluation of categories for AI application to Networks
- GR ENI 016 Functional Concepts for Modular System Operation
- GR ENI 017 Overview of Prominent Control Loop Architectures
- GR ENI 018 Artificial Intelligence Mechanisms for Modular Systems
From the Systems Architecture Spec:
The ENI System is an innovative, policy-based, model-driven functional architecture that improves operator experience. In addition to network automation, the ENI System assists decision-making of humans as well as machines, to enable a more maintainable and reliable system that provides context-aware services that more efficiently meet the needs of the business.
For example, the ENI System enables the network to change its behaviour (e.g. the set of services offered) in accordance with changes in context, including business goals, environmental conditions, and the varying needs of end-users.
This is achieved by using policy-driven closed control loops that use emerging technologies, such as big data analysis, analytics, and artificial intelligence mechanisms, to adjust the configuration and monitoring of networks and networked applications. It dynamically updates its acquired knowledge to understand the environment, including the needs of end-users and the goals of the operator, by learning from actions taken under its direction as well as those from other machines and humans (i.e. it is an experiential architecture).
It also ensures that automated decisions taken by the ENI System are correct and increase the reliability and stability, and lower the maintenance required, of the network and the applications that it supports. It improves and simplifies the management of network services through their visualization, and enables the discovery of otherwise hidden trends and interdependencies.
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