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When and how to update AI/ML models in 6G resource-constrained network domains?

Brito, Flavio ; Cisneros, Josue Castaneda ; Ghebretensae, Zere ; Linder, Neiva LU and Odling, Per LU (2024) 2024 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2024 p.937-942
Abstract

Artificial intelligence and Machine Learning (AI/ML) are steadily becoming widespread across all layers in the current mobile network generation. The next generation of networks, namely 6G, will consider AI/ML as a foundational block to deliver all expected results of such networks. Thus, the management of AI/ML applications across different layers of 6G is paramount for the success of the next generation of mobile networks. However, current approaches to the management of AI/ML, namely Machine Learning Operations (MLOps), focus mostly on independent AI/ML operations for a specific problem with the goal of improving the performance of the overall models of the AI/ML application. While this approach is useful for scenarios where... (More)

Artificial intelligence and Machine Learning (AI/ML) are steadily becoming widespread across all layers in the current mobile network generation. The next generation of networks, namely 6G, will consider AI/ML as a foundational block to deliver all expected results of such networks. Thus, the management of AI/ML applications across different layers of 6G is paramount for the success of the next generation of mobile networks. However, current approaches to the management of AI/ML, namely Machine Learning Operations (MLOps), focus mostly on independent AI/ML operations for a specific problem with the goal of improving the performance of the overall models of the AI/ML application. While this approach is useful for scenarios where resources are plentiful, such as the cloud layer, in resource-constrained network domains, focusing only on performance is not the best approach. For example, in the network edge domain, resources such as energy and computation are limited; thus, when and how to update an AI/ML model is a critical question to answer. In this paper, we focus on MLOps in scenarios with limited resources. To this end, we propose a network component for 6G, namely the Model Manager. This component automatically decides when and how to update a given AI/ML model based on both performance and resource consumption points of view. For this, we introduce a Model Manager model score to decide the approach to updating an AI/ML model. Our experiments show that by using this score, the model manager could find suitable situations on how to update a model without manual configuration.

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author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
6G, AI/ML, MLOps, Mobile Networks
host publication
2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2024 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2024
conference location
Antwerp, Belgium
conference dates
2024-06-03 - 2024-06-06
external identifiers
  • scopus:85199885674
ISBN
9798350344998
DOI
10.1109/EuCNC/6GSummit60053.2024.10597011
language
English
LU publication?
yes
id
7672a40e-e76b-4fbc-834d-912af5042b12
date added to LUP
2024-11-11 15:39:21
date last changed
2025-04-04 14:51:20
@inproceedings{7672a40e-e76b-4fbc-834d-912af5042b12,
  abstract     = {{<p>Artificial intelligence and Machine Learning (AI/ML) are steadily becoming widespread across all layers in the current mobile network generation. The next generation of networks, namely 6G, will consider AI/ML as a foundational block to deliver all expected results of such networks. Thus, the management of AI/ML applications across different layers of 6G is paramount for the success of the next generation of mobile networks. However, current approaches to the management of AI/ML, namely Machine Learning Operations (MLOps), focus mostly on independent AI/ML operations for a specific problem with the goal of improving the performance of the overall models of the AI/ML application. While this approach is useful for scenarios where resources are plentiful, such as the cloud layer, in resource-constrained network domains, focusing only on performance is not the best approach. For example, in the network edge domain, resources such as energy and computation are limited; thus, when and how to update an AI/ML model is a critical question to answer. In this paper, we focus on MLOps in scenarios with limited resources. To this end, we propose a network component for 6G, namely the Model Manager. This component automatically decides when and how to update a given AI/ML model based on both performance and resource consumption points of view. For this, we introduce a Model Manager model score to decide the approach to updating an AI/ML model. Our experiments show that by using this score, the model manager could find suitable situations on how to update a model without manual configuration.</p>}},
  author       = {{Brito, Flavio and Cisneros, Josue Castaneda and Ghebretensae, Zere and Linder, Neiva and Odling, Per}},
  booktitle    = {{2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)}},
  isbn         = {{9798350344998}},
  keywords     = {{6G; AI/ML; MLOps; Mobile Networks}},
  language     = {{eng}},
  pages        = {{937--942}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{When and how to update AI/ML models in 6G resource-constrained network domains?}},
  url          = {{http://dx.doi.org/10.1109/EuCNC/6GSummit60053.2024.10597011}},
  doi          = {{10.1109/EuCNC/6GSummit60053.2024.10597011}},
  year         = {{2024}},
}