Date
Attendees
LF Staff: Jim Baker Kenny Paul, Trishan de Lanerolle , Brandon Wick
Committee Members: Massimo Banzi Fernando Oliveira, Kodi Atuchukwu
Proxies:
TAC: Ranny Haiby, Martin Jackson, Georg Kunz, Christian Olrog, Frank Brockners , Al Morton , Brian Freeman , Jason Hunt, Olaf Renner
Guests: mazin gilbert @Anil Kapur, Tina Tsou , Vishnu Ram
Agenda
- Start the Recording
- Antitrust Policy
- Agenda Bashing (Roll Call, Action Items (5 minutes)
- General Topics
- Review the vDTF notes and set priorities
- Co-meet with TAC and discuss AI/ML data sharing project
Minutes
Questions:
- What else would you expect regarding AI with ONAP? maybe start from our Control Loop mechanisms and then add AI/ML?
- Is there any network automation/autonomy use-case specific model that could be enabled by Acumos Market?
- What would an OVP3.0 test suite test?
- How can we validate that the algorithms actually work in production?
- What is the TM Forum Spec for network intelligence ? IG1230 TMF ANP
- What is the common platform and where should it be hosted?
- What types of labs do we actually need?
- How can Operators share data with open source communities to create algorithms or even models?
- Anonymous data sharing issue has been solved for the medical industry stripping out HIPPA data, etc. That is a much bigger problem than sharing networking data.
- What are the expectations Operators that create their own AI algorithms and models themselves have from open source communities, is it just the generic Data Analytics Framework platform?
- What AI/ML testing already exists with the Operators
- Ranny Haiby ia aware of some work that has been done:
- Congestion prediction and mitigation - This use case will demonstrate how AI/ML may be used to predict congestion and perform closed loop automation for executing configuration changes to mitigate.
- Sleeper Cell Detection - Predict a cell going to "sleep" and handover a critical UE (e.g. ambulance) to another cell.
- Traffic Steering - Improve Quality of Experience (QoE) by steering UE traffic among multiple cells.
- Ranny Haiby ia aware of some work that has been done:
- Massimo Banzi shared info on TIM Big Data challenges - (data lake no longer exists) https://www.slideshare.net/rajeshwerkushwaha/telecom-italia-big-data-challenge
- Also pointed out this project: https://www.opalproject.org/about-opal
- TMF has AI & DAta Analytiucs project. An initiative is is producing an AI Model Data Sheet, Anaother iniziative is on Data Governance with a Data Governance White Paper ( IG 1225)
- @Vishnu - ITU-T ran a AI/ML in 5G Challenge in 2020: https://www.itu.int/en/ITU-T/AI/challenge/2020/Pages/default.aspx There are a bunch of problem statements (use cases) and data, from operators.
- Brian Freeman some work and royalties may be required to make this work
- Possible approaches
- is to select a single usecase and use that to understand the challenges around sanitizing the data
- create a lab that can be used by the operators to test algorithms
- sharing models and algorithms only
- share "insights", much like security threat analytics might be shared. Depends on use case. Perhaps network resiliency or fraud use cases would apply to insight sharing?
- Can any usecases be inferred from the survey at all? Question #8 most operators already have AI efforts in play for all of these items.
POSSIBLE USECASES:
- Sleeper cell detection
- Congestion prediction and mitigation
- Traffic Steering
Action items
- Jim Baker create a wiki page for data consolidation