research-article

kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices

Authors:

Sergi Alcalá-Marín,

Antonio Bazco-Nogueras,

Marco FioreAuthors Info & Claims

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

https://doi.org/10.1145/3565287.3610265

Published: 16 October 2023 Publication History

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.

References

[1]

S. Arora and A. Ksentini. 2021. Dynamic Resource Allocation and Placement of Cloud Native Network Services. In IEEE Int. Conf. Commun. (ICC). 1--6.

[2]

D. Bega et al. 2019. DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning. In Proc. of IEEE INFOCOM. 1--9.

Digital Library

[3]

D. Bega, M. Gramaglia, A. B., V. Sciancalepore, K. Samdanis, and X. Costa-Perez. 2017. Optimising 5G infrastructure markets: The business of network slicing. In Proc. of IEEE INFOCOM. 1--9.

[4]

W. Ben-Ameur, L. Cano, and T. Chahed. 2021. A framework for joint admission control, resource allocation and pricing for network slicing in 5G. In 2021 IEEE Global Communications Conf. (GLOBECOM). 1--6.

[5]

M. Burman and M. Gall. 2022. Ericsson and Red Hat empower service providers to build multi-vendor networks.

[6]

P. Caballero, A. Banchs, G. de Veciana, and X. Costa-Pérez. 2017. Multi-Tenant Radio Access Network Slicing: Statistical Multiplexing of Spatial Loads. IEEE/ACM Transactions on Networking 25, 5 (2017), 3044--3058.

Digital Library

[7]

P. Caballero, A. Banchs, G. de Veciana, X. Costa-Pérez, and A. Azcorra. 2018. Network Slicing for Guaranteed Rate Services: Admission Control and Resource Allocation Games. IEEE Transactions on Wireless Communications 17, 10 (2018), 6419--6432.

Digital Library

[8]

S. Gholamipour, B. Akbari, N. Mokari, M. M. Tajiki, and E. A. Jorswieck. 2021. Online Admission Control and Resource Allocation in Network Slicing under Demand Uncertainties.

[9]

D. Giannopoulos, P. Papaioannou, C. Tranoris, and S. Denazis. 2021. Monitoring as a Service over a 5G Network Slice. In Joint European Conf. on Networks and Commun. & 6G Summit (EuCNC/6G Summit). 329--334.

[10]

B. Han, V. Sciancalepore, D. Feng, X. Costa-Perez, and Hans D. Schotten. 2019. A Utility-Driven Multi-Queue Admission Control Solution for Network Slicing. In Proc. of IEEE INFOCOM. 55--63.

[11]

Y. Hua, R. Li, Z. Zhao, X. Chen, and H. Zhang. 2020. GAN-Powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing. IEEE J. Selected Areas in Communications 38, 2 (2020), 334--349.

[12]

J. A. Hurtado Sánchez, K. Casilimas, and O. M. Caicedo Rendon. 2022. Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey. Sensors 22, 8 (2022).

[13]

J. Koo, V. B. Mendiratta, M. R. Rahman, and A. Walid. 2019. Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics. In Int. Conf. on Network and Service Management (CNSM). 1--5.

[14]

Q. Liu, T. Han, and E. Moges. 2020. EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning. In IEEE Int. Conf. on Distributed Computing Systems (ICDCS). 234--244.

[15]

M. Liwang, X. Wang, and R. Chen. 2022. Computing Resource Provisioning at the Edge: An Overbooking-Enabled Trading Paradigm. IEEE Wireless Commun. 29, 5 (2022), 68--76.

[16]

L. Lo Schiavo, M. Fiore, M. Gramaglia, A. Banchs, and X. Costa-Perez. 2022. Forecasting for Network Management with Joint Statistical Modelling and Machine Learning. (2022).

[17]

Z. Luo, C. Wu, Z. Li, and W. Zhou. 2019. Scaling Geo-Distributed Network Function Chains: A Prediction and Learning Framework. IEEE J. Selected Areas in Communications 37, 8 (2019), 1838--1850.

Digital Library

[18]

Q. T. Luu, S. Kerboeuf, and M. Kieffer. 2021. Uncertainty-Aware Resource Provisioning for Network Slicing. IEEE Transactions on Network and Service Management 18, 1 (2021), 79--93.

Digital Library

[19]

S. Makridakis, E. Spiliotis, and V. Assimakopoulos. 2020. The M4 Competition: 100,000 time series and 61 forecasting methods. Int. Journal of Forecasting 36, 1 (2020), 54 -- 74.

[20]

C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Pérez. 2019. Resource Sharing Efficiency in Network Slicing. IEEE Transactions on Network and Service Management 16, 3 (2019), 909--923.

[21]

S. Martello. 1990. Knapsack Problems: Algorithms and Computer Implementations. Wiley-Interscience series in discrete mathematics and optimiza tion (1990).

Digital Library

[22]

S. Martello and P. Toth. 1990. Knapsack Problems: Algorithms and Computer Implementations. Wiley. https://books.google.es/books?id=0dhQAAAAMAAJ

Digital Library

[23]

F. Patzelt. 2022. Colored Noise. https://github.com/felixpatzelt/colorednoise.

[24]

A. Pino, P. Khodashenas, X. Hesselbach, E. Coronado, and S. Siddiqui. 2021. Validation and Benchmarking of CNFs in OSM for pure Cloud Native applications in 5G and beyond. In 2021 Int. Conf. on Computer Communications and Networks (ICCCN). 1--9.

[25]

JE Rachid and J Erfanian. 2015. NGMN 5G Initiative White Paper.

[26]

J. X. Salvat, L. Zanzi, A. Garcia-Saavedra, V. Sciancalepore, and X. Costa-Perez. 2018. Overbooking Network Slices through Yield-Driven End-to-End Orchestration. In Proc. Int. Conf. Emerging Networking EXperiments and Technologies (CoNEXT). 353--365.

[27]

S. Saxena and K. M. Sivalingam. 2022. Slice admission control using overbooking for enhancing provider revenue in 5G Networks. In NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium. 1--7.

[28]

C. Sexton, N. Marchetti, and L. A. DaSilva. 2020. On Provisioning Slices and Overbooking Resources in Service Tailored Networks of the Future. IEEE/ACM Transactions on Networking 28, 5 (2020), 2106--2119.

Digital Library

[29]

S. D. A. Shah, M. A. Gregory, and S. Li. 2021. Cloud-Native Network Slicing Using Software Defined Networking Based Multi-Access Edge Computing: A Survey. IEEE Access 9 (2021), 10903--10924.

[30]

K. T. Talluri and G. Van Ryzin. 2004. The theory and practice of revenue management. Vol. 1. Springer.

[31]

Z. Tang, F. Zhang, X. Zhou, W. Jia, and W. Zhao. 2022. Pricing Model for Dynamic Resource Overbooking in Edge Computing. IEEE Transactions on Cloud Computing (2022).

[32]

The Linux Foundation. 2022. The Linux Foundation and Google Cloud Launch Nephio to Enable and Simplify Cloud Native Automation of Telecom Network Functions. Consulted on March 10th 2023.

[33]

D. Tikunov and T. Nishimura. 2007. Traffic prediction for mobile network using Holt-Winter's exponential smoothing. In Int. Conf. on Software, Telecommunications and Computer Networks. IEEE, 1--5.

[34]

S. Troia, R. Alvizu, and G. Maier. 2019. Reinforcement Learning for Service Function Chain Reconfiguration in NFV-SDN Metro-Core Optical Networks. IEEE Access 7 (2019), 167944--167957.

[35]

N. Van Huynh, D. Thai Hoang, D. N. Nguyen, and E. Dutkiewicz. 2019. Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks. IEEE J. Selected Areas in Communications 37, 6 (2019), 1455--1470.

[36]

L. Zanzi, J. X. Salvat, V. Sciancalepore, A. Garcia-Saavedra, and X. Costa-Perez. 2018. Overbooking Network Slices End-to-End: Implementation and Demonstration. In Proc. of ACM SIGCOMM. 144--146.

[37]

J. Zheng, P. Caballero, G. de Veciana, S. J. Baek, and A. Banchs. 2018. Statistical Multiplexing and Traffic Shaping Games for Network Slicing. IEEE/ACM Transactions on Networking 26, 6 (2018), 2528--2541.

Digital Library

[38]

X. Zhou, R. Li, T. Chen, and H. Zhang. 2016. Network slicing as a service: enabling enterprises' own software-defined cellular networks. IEEE Communications Magazine 54, 7 (2016), 146--153.

Digital Library

Cited By

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
https://doi.org/10.1109/TNSM.2024.3377442

Index Terms

kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices
1. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Network resources allocation
      2. Traffic engineering algorithms

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Published In

cover image ACM Conferences

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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Published: 16 October 2023

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Horizon 2020 Framework Programme
Regional Government of Madrid
UNICO 5G I+D

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SIGMOBILE

MobiHoc '23: Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing

October 23 - 26, 2023

DC, Washington, USA

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Cited By

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
https://doi.org/10.1109/TNSM.2024.3377442

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