Forecasting and anticipating SLO breaches in programmable networks

Jaafar Bendriss; Imen Grida Ben Yahia; Djamal Zeghlache

doi:10.1109/ICIN.2017.7899402

Forecasting and anticipating SLO breaches in programmable networks

Jaafar Bendriss
, Imen Grida Ben Yahia
, Djamal Zeghlache

Orange Labs

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

Abstract

Software Networks built by combining Software Defined Networks (SDN), Network Function Virtualization (NFV) and Cloud principles call for agile and dynamic automation of management operations to ensure continuous provisioning and deployment of networked resources and services. In this context, efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become essential to fulfill established service contracts with clients. In this paper, we design and specify a framework for cognitive SLA enforcement (using Artificial Neural Network learning) for networking services involving VNFs (Virtualized Network Functions) and SDN controllers. A proof of concept, a testbed description and an extensive evaluation assess the performance of the proposed framework.

Original language	English
Title of host publication	Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017
Editors	Stefano Secci, Noel Crespi, Antonio Manzalini
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	127-134
Number of pages	8
ISBN (Electronic)	9781509036721
DOIs	https://doi.org/10.1109/ICIN.2017.7899402
Publication status	Published - 13 Apr 2017
Event	20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017 - Paris, France Duration: 7 Mar 2017 → 9 Mar 2017

Publication series

Name	Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017

Conference

Conference	20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017
Country/Territory	France
City	Paris
Period	7/03/17 → 9/03/17

Keywords

ANN
Cognitive Management
Machine Learning
NFV
Network Management
SDN
SLA
SLO

Access to Document

10.1109/ICIN.2017.7899402

Cite this

Bendriss, J., Ben Yahia, I. G., & Zeghlache, D. (2017). Forecasting and anticipating SLO breaches in programmable networks. In S. Secci, N. Crespi, & A. Manzalini (Eds.), Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017 (pp. 127-134). Article 7899402 (Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIN.2017.7899402

Bendriss, Jaafar ; Ben Yahia, Imen Grida ; Zeghlache, Djamal. / Forecasting and anticipating SLO breaches in programmable networks. Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017. editor / Stefano Secci ; Noel Crespi ; Antonio Manzalini. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 127-134 (Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017).

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title = "Forecasting and anticipating SLO breaches in programmable networks",

abstract = "Software Networks built by combining Software Defined Networks (SDN), Network Function Virtualization (NFV) and Cloud principles call for agile and dynamic automation of management operations to ensure continuous provisioning and deployment of networked resources and services. In this context, efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become essential to fulfill established service contracts with clients. In this paper, we design and specify a framework for cognitive SLA enforcement (using Artificial Neural Network learning) for networking services involving VNFs (Virtualized Network Functions) and SDN controllers. A proof of concept, a testbed description and an extensive evaluation assess the performance of the proposed framework.",

keywords = "ANN, Cognitive Management, Machine Learning, NFV, Network Management, SDN, SLA, SLO",

author = "Jaafar Bendriss and \{Ben Yahia\}, \{Imen Grida\} and Djamal Zeghlache",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017 ; Conference date: 07-03-2017 Through 09-03-2017",

year = "2017",

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doi = "10.1109/ICIN.2017.7899402",

language = "English",

series = "Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017",

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

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Bendriss, J, Ben Yahia, IG & Zeghlache, D 2017, Forecasting and anticipating SLO breaches in programmable networks. in S Secci, N Crespi & A Manzalini (eds), Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017., 7899402, Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017, Institute of Electrical and Electronics Engineers Inc., pp. 127-134, 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017, Paris, France, 7/03/17. https://doi.org/10.1109/ICIN.2017.7899402

Forecasting and anticipating SLO breaches in programmable networks. / Bendriss, Jaafar; Ben Yahia, Imen Grida; Zeghlache, Djamal.
Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017. ed. / Stefano Secci; Noel Crespi; Antonio Manzalini. Institute of Electrical and Electronics Engineers Inc., 2017. p. 127-134 7899402 (Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017).

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

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N2 - Software Networks built by combining Software Defined Networks (SDN), Network Function Virtualization (NFV) and Cloud principles call for agile and dynamic automation of management operations to ensure continuous provisioning and deployment of networked resources and services. In this context, efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become essential to fulfill established service contracts with clients. In this paper, we design and specify a framework for cognitive SLA enforcement (using Artificial Neural Network learning) for networking services involving VNFs (Virtualized Network Functions) and SDN controllers. A proof of concept, a testbed description and an extensive evaluation assess the performance of the proposed framework.

AB - Software Networks built by combining Software Defined Networks (SDN), Network Function Virtualization (NFV) and Cloud principles call for agile and dynamic automation of management operations to ensure continuous provisioning and deployment of networked resources and services. In this context, efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become essential to fulfill established service contracts with clients. In this paper, we design and specify a framework for cognitive SLA enforcement (using Artificial Neural Network learning) for networking services involving VNFs (Virtualized Network Functions) and SDN controllers. A proof of concept, a testbed description and an extensive evaluation assess the performance of the proposed framework.

KW - ANN

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KW - Machine Learning

KW - NFV

KW - Network Management

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PB - Institute of Electrical and Electronics Engineers Inc.

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Bendriss J, Ben Yahia IG, Zeghlache D. Forecasting and anticipating SLO breaches in programmable networks. In Secci S, Crespi N, Manzalini A, editors, Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 127-134. 7899402. (Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017). doi: 10.1109/ICIN.2017.7899402

Forecasting and anticipating SLO breaches in programmable networks

Abstract

Publication series

Conference

Keywords

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Cite this