research-article
Authors: Sergi Alcalá-Marín, Antonio Bazco-Nogueras, Albert Banchs, Marco Fiore
MobiHoc '23: Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
Pages 51 - 60
Published: 16 October 2023 Publication History
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Abstract
Cloud-native mobile networks pave the road for Network Slicing as a Service (NSaaS), where slice overbooking is a promising management strategy to maximize the revenues from admitted slices by exploiting the fact they are unlikely to fully utilize their reserved resources concurrently. While seminal works have shown the potential of overbooking for NSaaS in simplistic cases, its realization is challenging in practical scenarios with realistic slice demands, where its actual performance remains to be tested. In this paper, we propose kaNSaaS, a complete solution for NSaaS management with slice overbooking that combines deep learning and classical optimization to jointly solve the key tasks of admission control and resource allocation. Experiments with large-scale measurement data of actual tenant demands show that kaNSaaS increases the network operator profits by 300% with respect to NSaaS management strategies that do not employ overbooking, while outperforming by more than 20% state-of-the-art overbooking-based approaches.
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Cited By
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- Collet ABazco-Nogueras ABanchs AFiore M(2024)Explainable and Transferable Loss Meta-Learning for Zero-Touch Anticipatory Network ManagementIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337744221:3(2802-2823)Online publication date: Jun-2024
Index Terms
kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices
Networks
Network algorithms
Control path algorithms
Network resources allocation
Traffic engineering algorithms
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Published In
MobiHoc '23: Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
October 2023
621 pages
ISBN:9781450399265
DOI:10.1145/3565287
- General Chairs:
- Jie Wu,
- Suresh Subramaniam,
- Program Chairs:
- Bo Ji,
- Carla Fabiana Chiasserini
Copyright © 2023 ACM.
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Sponsors
- SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 October 2023
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Author Tags
- network slicing
- 5G
- forecasting
- optimization
- overbooking
Qualifiers
- Research-article
Funding Sources
- Horizon 2020 Framework Programme
- Regional Government of Madrid
- UNICO 5G I+D
Conference
MobiHoc '23
Sponsor:
- SIGMOBILE
October 23 - 26, 2023
DC, Washington, USA
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Overall Acceptance Rate 296 of 1,843 submissions, 16%
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Cited By
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- Collet ABazco-Nogueras ABanchs AFiore M(2024)Explainable and Transferable Loss Meta-Learning for Zero-Touch Anticipatory Network ManagementIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337744221:3(2802-2823)Online publication date: Jun-2024
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