Date

Attendees

LF Staff:  Jim Baker Kenny Paul, Trishan de Lanerolle , Brandon Wick
Committee Members: Massimo Banzi Fernando Oliveira, Kodi Atuchukwu
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.
  • 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