Intelligent Networking, AI and Machine Learning White Paper - A Telecommunications Operator’s Perspective
Contents
4.Survey Says-Current State of the Industry
5.Intelligent Networking Challenges
7.Next Steps and Recommended Actions
8.Conclusion and Call to Action
Revision History and control
Rev | Date | Author | Description | Reviewed | Approved by |
v10 | 27 Sept 2021 | Beth Cohen (Verizon) | Edited for readability – Final Review Needed |
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v09 | 26 Sept 2021 | Beth Cohen (Verizon) | Edited all the materials together |
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v08 | 14 Sep 2021 | Yuhan Zhang（CMCC） Lingli Deng(CMCC) Kaixi Liu(CMCC) | Revising the structure and content of the white paper Adding recommendations for the intelligent networking transformation |
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V07 | 17 Aug 2021 | Yuhan Zhang（CMCC） | merged with v06 |
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V06 | 03 Aug 2021 | Beth Cohen (Verizon) | Updates by Steve Casey (Verizon) Not merged with the work by Yuhan yes. |
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V05 | 03 Aug 2021 | Yuhan Zhang（CMCC） | Recommendation for network intelligence testing and certification | EUAG Team |
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V04 | 20 Jul 2021 | Yuhan Zhang（CMCC） | survey results, challenge | EUAG Team |
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V03 | 24 May 2021 | Massimo Banzi (TI) Beth Cohen (Verizon) Steve Casey (Verizon) | Definition sections, survey analysis | EUAG Team | Uploaded |
V01 | 15Apr2021 | Beth Cohen (Verizon) | Creation of original document and outline of paper. | EUAG Team |
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V02 | 13May2021 | Beth Cohen (Verizon) | Outline with some text. Asking for input from the EUAG Team | EUAG Team |
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White Paper Assumptions and Overall Objectives:
Audience: Technical and strategic leaders in the Telecom Industry interested in understanding the role of intelligent tools, AI and machine learning can have in support of their network infrastructure, customer .
7-10 Pages in length total – possibly will be some separate related papers on specific use cases and other information that does not fit into this one
Do not declare or propose a definite solution just share EUAG Operator community point of view
Purpose of White Paper is to share information about EUAG member companies’ approaches to network intelligence
Paper Overview
Based on the findings of a LFN sponsored survey of over 60 operators and vendors in the telecom segment, demonstrate the current state of adoption of the AI/ML technology for operators.
Make recommendations for where Open Source communities should be focusing their resources to further the intelligent networking efforts.
Call to action: Market and executive orientation. Purpose is to get people excited about Intelligent Networking as emerging technology and its importance, yet have an understanding of where the gaps are in both the technology and its adoption.. Bullet list of gaps. 2-3 bullet items. Identify missing Open-Source tools and potential projects.
Survey - AI/ML Survey (Result & Analysis)
@Yuhan Zhang @Beth Cohen
Uploaded current version of paper
Table for contributors to white paper and status
Contributors/CSPs | Sections | Status | Comments |
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@Beth Cohen (Verizon) | Section 1-9 | Created first draft | Posted first draft to this page. |
Yuhan Zhang (China Mobile) | Section 1-9 |
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@Lei Huang (China Mobile) | Section 1-9 |
| Created workspace in confluence page |
@Massimo Massimo (STC) | TBA |
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Steve Casey (Verizon) | TBA |
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Timeline
Work Items | Time |
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Complete the paper final draft | Sep 13th,2021 |
EUAG review and final version | Sep 17th,2021 |
Share a form paper MAC magic | Sep 24th,2021 |
Publication | Oct 8th,2021 |
ONES Summit White Paper Webinars | Oct & Nov,2021 |