Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism

Khizar Abbas; Talha Ahmed Khan; Muhammad Afaq; Wang Cheol Song

doi:10.1109/NOMS54207.2022.9789706

Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism

Khizar Abbas, Talha Ahmed Khan, Muhammad Afaq, Wang Cheol Song^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Scopus citations

Abstract

5G technology come up with many innovative features compared to legacy networks, such as network slicing that envisioned a wide variety of services from different customers, network operators, and industrial verticals. Network slicing ensures dedicated and isolated resources to each of the services. The autonomous orchestration and management of end-to-end (e2e) network slicing is critical due to the complex network configuration for the underlying infrastructure. On the other side, data analytics seems promising to manage and control the underlying network resources proactively. So, network data analytics function (NWDAF) has been introduced in 5G service-based architecture (SBA), which enables network operators to use various artificial intelligence (AI) and machine learning (ML) techniques. Therefore, this paper presents a closed-loop mechanism that has two parts: 1) an intent-based networking (IBN) mechanism for efficient control, orchestration, and management of e2e network slicing. 2) A data analytics mechanism that uses novel hybrid ensemble learning (EL) algorithms for network resource utilization prediction and anomaly detection and mitigation. The results show that the proposed stacking ensemble learning (STEL) model for resource utilization prediction enhanced accuracy by approximately up to 20% and reduced the error by 45% compared to the state-of-the-art models. In addition, ML models assist the IBN platform in updating and managing the network resources proactively.

Original language	English
Title of host publication	Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022
Subtitle of host publication	Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022
Editors	Pal Varga, Lisandro Zambenedetti Granville, Alex Galis, Istvan Godor, Noura Limam, Prosper Chemouil, Jerome Francois, Marc-Oliver Pahl
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665406017
DOIs	https://doi.org/10.1109/NOMS54207.2022.9789706
State	Published - 2022
Externally published	Yes
Event	2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022 - Budapest, Hungary Duration: 25 Apr 2022 → 29 Apr 2022

Publication series

Name	Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022

Conference

Conference	2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022
Country/Territory	Hungary
City	Budapest
Period	25/04/22 → 29/04/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

5G
AI
IBN
ML
NWDAF
Network slicing
ensemble learning

ASJC Scopus subject areas

Management of Technology and Innovation
Artificial Intelligence
Computer Networks and Communications
Hardware and Architecture
Software
Information Systems and Management
Safety, Risk, Reliability and Quality

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/NOMS54207.2022.9789706

Cite this

Abbas, K., Khan, T. A., Afaq, M., & Song, W. C. (2022). Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism. In P. Varga, L. Z. Granville, A. Galis, I. Godor, N. Limam, P. Chemouil, J. Francois, & M.-O. Pahl (Eds.), Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022 (Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NOMS54207.2022.9789706

Abbas, Khizar ; Khan, Talha Ahmed ; Afaq, Muhammad et al. / Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management : An Intent-Based Networking Mechanism. Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. editor / Pal Varga ; Lisandro Zambenedetti Granville ; Alex Galis ; Istvan Godor ; Noura Limam ; Prosper Chemouil ; Jerome Francois ; Marc-Oliver Pahl. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022).

@inproceedings{ebefd574981b43c1aedf516ff88c74d4,

title = "Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism",

abstract = "5G technology come up with many innovative features compared to legacy networks, such as network slicing that envisioned a wide variety of services from different customers, network operators, and industrial verticals. Network slicing ensures dedicated and isolated resources to each of the services. The autonomous orchestration and management of end-to-end (e2e) network slicing is critical due to the complex network configuration for the underlying infrastructure. On the other side, data analytics seems promising to manage and control the underlying network resources proactively. So, network data analytics function (NWDAF) has been introduced in 5G service-based architecture (SBA), which enables network operators to use various artificial intelligence (AI) and machine learning (ML) techniques. Therefore, this paper presents a closed-loop mechanism that has two parts: 1) an intent-based networking (IBN) mechanism for efficient control, orchestration, and management of e2e network slicing. 2) A data analytics mechanism that uses novel hybrid ensemble learning (EL) algorithms for network resource utilization prediction and anomaly detection and mitigation. The results show that the proposed stacking ensemble learning (STEL) model for resource utilization prediction enhanced accuracy by approximately up to 20\% and reduced the error by 45\% compared to the state-of-the-art models. In addition, ML models assist the IBN platform in updating and managing the network resources proactively.",

keywords = "5G, AI, IBN, ML, NWDAF, Network slicing, ensemble learning",

author = "Khizar Abbas and Khan, \{Talha Ahmed\} and Muhammad Afaq and Song, \{Wang Cheol\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022 ; Conference date: 25-04-2022 Through 29-04-2022",

year = "2022",

doi = "10.1109/NOMS54207.2022.9789706",

language = "English",

series = "Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Pal Varga and Granville, \{Lisandro Zambenedetti\} and Alex Galis and Istvan Godor and Noura Limam and Prosper Chemouil and Jerome Francois and Marc-Oliver Pahl",

booktitle = "Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022",

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Abbas, K, Khan, TA, Afaq, M & Song, WC 2022, Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism. in P Varga, LZ Granville, A Galis, I Godor, N Limam, P Chemouil, J Francois & M-O Pahl (eds), Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022, Budapest, Hungary, 25/04/22. https://doi.org/10.1109/NOMS54207.2022.9789706

Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism. / Abbas, Khizar; Khan, Talha Ahmed; Afaq, Muhammad et al.
Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. ed. / Pal Varga; Lisandro Zambenedetti Granville; Alex Galis; Istvan Godor; Noura Limam; Prosper Chemouil; Jerome Francois; Marc-Oliver Pahl. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management

T2 - 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022

AU - Abbas, Khizar

AU - Khan, Talha Ahmed

AU - Afaq, Muhammad

AU - Song, Wang Cheol

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N2 - 5G technology come up with many innovative features compared to legacy networks, such as network slicing that envisioned a wide variety of services from different customers, network operators, and industrial verticals. Network slicing ensures dedicated and isolated resources to each of the services. The autonomous orchestration and management of end-to-end (e2e) network slicing is critical due to the complex network configuration for the underlying infrastructure. On the other side, data analytics seems promising to manage and control the underlying network resources proactively. So, network data analytics function (NWDAF) has been introduced in 5G service-based architecture (SBA), which enables network operators to use various artificial intelligence (AI) and machine learning (ML) techniques. Therefore, this paper presents a closed-loop mechanism that has two parts: 1) an intent-based networking (IBN) mechanism for efficient control, orchestration, and management of e2e network slicing. 2) A data analytics mechanism that uses novel hybrid ensemble learning (EL) algorithms for network resource utilization prediction and anomaly detection and mitigation. The results show that the proposed stacking ensemble learning (STEL) model for resource utilization prediction enhanced accuracy by approximately up to 20% and reduced the error by 45% compared to the state-of-the-art models. In addition, ML models assist the IBN platform in updating and managing the network resources proactively.

AB - 5G technology come up with many innovative features compared to legacy networks, such as network slicing that envisioned a wide variety of services from different customers, network operators, and industrial verticals. Network slicing ensures dedicated and isolated resources to each of the services. The autonomous orchestration and management of end-to-end (e2e) network slicing is critical due to the complex network configuration for the underlying infrastructure. On the other side, data analytics seems promising to manage and control the underlying network resources proactively. So, network data analytics function (NWDAF) has been introduced in 5G service-based architecture (SBA), which enables network operators to use various artificial intelligence (AI) and machine learning (ML) techniques. Therefore, this paper presents a closed-loop mechanism that has two parts: 1) an intent-based networking (IBN) mechanism for efficient control, orchestration, and management of e2e network slicing. 2) A data analytics mechanism that uses novel hybrid ensemble learning (EL) algorithms for network resource utilization prediction and anomaly detection and mitigation. The results show that the proposed stacking ensemble learning (STEL) model for resource utilization prediction enhanced accuracy by approximately up to 20% and reduced the error by 45% compared to the state-of-the-art models. In addition, ML models assist the IBN platform in updating and managing the network resources proactively.

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DO - 10.1109/NOMS54207.2022.9789706

M3 - Conference contribution

AN - SCOPUS:85133194442

T3 - Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022

BT - Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022

A2 - Varga, Pal

A2 - Granville, Lisandro Zambenedetti

A2 - Galis, Alex

A2 - Godor, Istvan

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A2 - Chemouil, Prosper

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A2 - Pahl, Marc-Oliver

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 April 2022 through 29 April 2022

ER -

Abbas K, Khan TA, Afaq M, Song WC. Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism. In Varga P, Granville LZ, Galis A, Godor I, Limam N, Chemouil P, Francois J, Pahl MO, editors, Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022). doi: 10.1109/NOMS54207.2022.9789706

Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Fingerprint

Cite this