The new network management architecture will use AI techniques and context-aware policies to adjust offered services based on changes in user needs, environmental conditions and business goals. The system is experiential, in that it learns from its operation and from decisions given to it by operators to improve its knowledge of how to act in the future.
It is anticipated such an AI-assisted approach will help operators automate their network configuration and monitoring processes, thereby reducing their operational expenditure and improving the use and maintenance of their networks.
Operators see human-machine interaction as slow, error-prone, expensive, and cumbersome. Programming different devices and building agile, personalized services makes it increasingly complex to integrate different standardized platforms in their network and operational environment. These human-machine interaction challenges are considered by operators as barriers to reducing the time to market of innovative and advanced services. They also lack an efficient and extensible standards-based mechanism to provide contextually-aware services (e.g., services that adapt to changes in user needs, business goals, or environmental conditions).
“The unique added value of the ETSI ISG ENI approach is to define new metrics to quantify the operator’s experience; this enables the optimization and adjustment of the operator’s experience over time, taking advantage of machine learning and reasoning.” says Ray Forbes, convenor of ETSI ISG ENI.