Topic Leader(s)
Topic Overview
EUAG working updates, what CSPs concerns this year and what we've achieved, including AI/ML, CSPs requirements priorities, automated testing white paper, etc. We will have three sub-topics in this session. This session will be a review of Operators' concerns this year and what we've achieved, including AI/ML, CSPs requirements priorities, automated testing white paper, etc. We will have three sub-topics in this session: EUAG Networking AI/ML Survey and Next Steps, EUAG Input from LFN Projects to set Priorities, EUAG Networking AI/ML Testing Requirements, NFV testing white paper, Anuket verification program vs. LFN level branding (nee: OVP)
Slides & Recording
EUAG-AI&ML_survey_results-2021-02-01.mp4
Minutes
Session is to review the previous work and ask for community feedback about the direction of the EUAG projects. Discussion of the survey methods and process. Initial results show that most companies are just starting their journey incorporating AI/ML into their networks. Catherine: We have integrated Acumos with ONAP. What else would you expect regarding AI with ONAP? maybe start from our Control Loop mechanisms and then add AI/ML? Acumos is an AI platform LF open source
www.Acumos.org ONAP: https://wiki.onap.org/display/DW/Acumos+DCAE+Integration has added some integration efforts.
Is there any network automation/autonomy use-case specific model that could be enabled by Acumos Market?
AL/ML testing session: Presentation from Lingli Deng
Insert slides here:
What does “network service certification” mean - functional validation? A: Autonomous network service, with self-healing, self-optimization, like capabilities, to 2B customers. other ref: lfaidata.foundation
What would an OVP3.0 test suite test? – Testing requirements should be the focus. RAN is one potential area, O-RAN is one potential option for RAN. O-RAN has its own OTIC certification program. I am not sure what we could help? I think the biggest challenge here will be how to divide up the testing. If testing is end-to-end, it will be hard to differentiate testing of the technologies or components from testing a specific instance of a deployment. Need to be able to create a framework that does not immediately devolve into a proprietary E2E scenario that would not be useful to the community. Can we break the components apart enough to allow the operators to get meaningful results out of it. How can we validate that the algorithms actually work in production? There are some experimental network for academic research inside and outside China. They are too way from production. It has to be possible. Otherwise these algorithms would never to put in scale adoption. I think to attract industry and business we need focus on specific use cases and then AI Tech details with it . I am sure it is very hard to push AI without some clear benefits and importantly their realization. We could share with the use-case specific priority next time.
should O-RAN be included here? xApps are all about intelligent network (though for the specific use case of RAN) – In a nutshell, yes. Might be other groups also looking at this. RAN work and any synergy with ours. i agree O-Ran should be included - they are planning embedding AI-powered radio control
February 2020 white paper was including AI/ML module February 2020 white paper was including AI/ML module
https://www.o-ran.org/s/O-RAN-Use-Cases-and-Deployment-Scenarios-Whitepaper-February-2020.pdf
What is the TM Forum Spec for network intelligence ? – IG1230 TMF ANP @Lingli - that’s a fair analysis. We should just acknowledge the O-RAN work and any synergy with ours.