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

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Conference article in proceedings
Date
2023
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Language
en
Pages
5
Series
2022 IEEE International Conference on Big Data (IEEE BigData 2022)
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.
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Citation
Truong , L & Nhu Trang , N N 2023 , HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks . in S Tsumoto , Y Ohsawa , L Chen , D Van den Poel , X Hu , Y Motomura , T Takagi , L Wu , Y Xie , A Abe & V Raghavan (eds) , 2022 IEEE International Conference on Big Data (IEEE BigData 2022) . IEEE , pp. 2389-2393 , IEEE International Conference on Big Data , Osaka , Japan , 17/12/2022 . https://doi.org/10.1109/BigData55660.2022.10020515