HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks

Linh Truong, Nguyen Ngoc Nhu Trang

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Abstract

This paper presents our approach for supporting machine learning (ML)-based analytics of quality of experience (QoE) related issues in a variety of Radio Access Networks (V-RAN). We focus on key problems in a holistic analytics infrastructure for engineers without strong ML skills and powerful computing infrastructures. We characterize types of relevant data and existing data systems to follow a specific data mesh approach suitable for engineers. The paper presents key steps in establishing the participation of engineers and the acquisition of domain knowledge. We introduce models for representing analytics subjects and their dependencies, and for managing relevant ML techniques and methods for analytics subjects. We explain our work through examples from a large-scale mobile network of approximately 4 million subscribers.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Big Data (IEEE BigData 2022)
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
PublisherIEEE
Pages2389-2393
Number of pages5
ISBN (Electronic)978-1-6654-8045-1
ISBN (Print)978-1-6654-8046-8
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventIEEE International Conference on Big Data - Osaka, Japan
Duration: 17 Dec 202220 Dec 2022

Conference

ConferenceIEEE International Conference on Big Data
Abbreviated titleBigData
Country/TerritoryJapan
CityOsaka
Period17/12/202220/12/2022

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