Discovering Alarm Correlation Rules for Network Fault Management

Philippe Fournier-Viger; Ganghuan He; Min Zhou; Mourad Nouioua; Jiahong Liu

doi:10.1007/978-3-030-76352-7_24

Discovering Alarm Correlation Rules for Network Fault Management

Philippe Fournier-Viger^*
, Ganghuan He
, Min Zhou
, Mourad Nouioua
, Jiahong Liu

^*Corresponding author for this work

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

8 Scopus citations

Abstract

Fault management is critical to telecommunication networks. It consists of detecting, diagnosing, isolating and fixing network problems, a task that is time-consuming. A promising approach to improve fault management is to find patterns revealing the relationships between network alarms, to then only show the most important alarms to network operators. However, a limitation of current algorithms of this type is that they ignore the network topology. But the network topology is important to understand how alarms propagate on a network. This paper addresses this issue by modeling a real-life telecommunication network as a dynamic attributed graph and then extracting correlation patterns between network alarms called Alarm Correlation Rules. Experiments on a large telecommunication network show that interesting patterns are found that can greatly compress the number of alarms presented to network operators, which can reduce network maintenance costs.

Original language	English
Title of host publication	Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings
Editors	Hakim Hacid, Fatma Outay, Hye-young Paik, Amira Alloum, Marinella Petrocchi, Mohamed Reda Bouadjenek, Amin Beheshti, Xumin Liu, Abderrahmane Maaradji
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	228-239
Number of pages	12
ISBN (Print)	9783030763510
DOIs	https://doi.org/10.1007/978-3-030-76352-7_24
State	Published - 2021
Externally published	Yes
Event	AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020 - Virtual, Online Duration: 14 Dec 2020 → 17 Dec 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12632 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020
City	Virtual, Online
Period	14/12/20 → 17/12/20

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Correlation patterns
Dynamic graph
Fault management

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-76352-7_24

Cite this

Fournier-Viger, P., He, G., Zhou, M., Nouioua, M., & Liu, J. (2021). Discovering Alarm Correlation Rules for Network Fault Management. In H. Hacid, F. Outay, H. Paik, A. Alloum, M. Petrocchi, M. R. Bouadjenek, A. Beheshti, X. Liu, & A. Maaradji (Eds.), Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings (pp. 228-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12632 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-76352-7_24

Fournier-Viger, Philippe ; He, Ganghuan ; Zhou, Min et al. / Discovering Alarm Correlation Rules for Network Fault Management. Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings. editor / Hakim Hacid ; Fatma Outay ; Hye-young Paik ; Amira Alloum ; Marinella Petrocchi ; Mohamed Reda Bouadjenek ; Amin Beheshti ; Xumin Liu ; Abderrahmane Maaradji. Springer Science and Business Media Deutschland GmbH, 2021. pp. 228-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b427679c21764b93ac6715609bfabc76,

title = "Discovering Alarm Correlation Rules for Network Fault Management",

abstract = "Fault management is critical to telecommunication networks. It consists of detecting, diagnosing, isolating and fixing network problems, a task that is time-consuming. A promising approach to improve fault management is to find patterns revealing the relationships between network alarms, to then only show the most important alarms to network operators. However, a limitation of current algorithms of this type is that they ignore the network topology. But the network topology is important to understand how alarms propagate on a network. This paper addresses this issue by modeling a real-life telecommunication network as a dynamic attributed graph and then extracting correlation patterns between network alarms called Alarm Correlation Rules. Experiments on a large telecommunication network show that interesting patterns are found that can greatly compress the number of alarms presented to network operators, which can reduce network maintenance costs.",

keywords = "Correlation patterns, Dynamic graph, Fault management",

author = "Philippe Fournier-Viger and Ganghuan He and Min Zhou and Mourad Nouioua and Jiahong Liu",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020 ; Conference date: 14-12-2020 Through 17-12-2020",

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doi = "10.1007/978-3-030-76352-7\_24",

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Fournier-Viger, P, He, G, Zhou, M, Nouioua, M & Liu, J 2021, Discovering Alarm Correlation Rules for Network Fault Management. in H Hacid, F Outay, H Paik, A Alloum, M Petrocchi, MR Bouadjenek, A Beheshti, X Liu & A Maaradji (eds), Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12632 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 228-239, AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020, Virtual, Online, 14/12/20. https://doi.org/10.1007/978-3-030-76352-7_24

Discovering Alarm Correlation Rules for Network Fault Management. / Fournier-Viger, Philippe; He, Ganghuan; Zhou, Min et al.
Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings. ed. / Hakim Hacid; Fatma Outay; Hye-young Paik; Amira Alloum; Marinella Petrocchi; Mohamed Reda Bouadjenek; Amin Beheshti; Xumin Liu; Abderrahmane Maaradji. Springer Science and Business Media Deutschland GmbH, 2021. p. 228-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12632 LNCS).

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

TY - GEN

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AU - He, Ganghuan

AU - Zhou, Min

AU - Nouioua, Mourad

AU - Liu, Jiahong

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N2 - Fault management is critical to telecommunication networks. It consists of detecting, diagnosing, isolating and fixing network problems, a task that is time-consuming. A promising approach to improve fault management is to find patterns revealing the relationships between network alarms, to then only show the most important alarms to network operators. However, a limitation of current algorithms of this type is that they ignore the network topology. But the network topology is important to understand how alarms propagate on a network. This paper addresses this issue by modeling a real-life telecommunication network as a dynamic attributed graph and then extracting correlation patterns between network alarms called Alarm Correlation Rules. Experiments on a large telecommunication network show that interesting patterns are found that can greatly compress the number of alarms presented to network operators, which can reduce network maintenance costs.

AB - Fault management is critical to telecommunication networks. It consists of detecting, diagnosing, isolating and fixing network problems, a task that is time-consuming. A promising approach to improve fault management is to find patterns revealing the relationships between network alarms, to then only show the most important alarms to network operators. However, a limitation of current algorithms of this type is that they ignore the network topology. But the network topology is important to understand how alarms propagate on a network. This paper addresses this issue by modeling a real-life telecommunication network as a dynamic attributed graph and then extracting correlation patterns between network alarms called Alarm Correlation Rules. Experiments on a large telecommunication network show that interesting patterns are found that can greatly compress the number of alarms presented to network operators, which can reduce network maintenance costs.

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BT - Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings

A2 - Hacid, Hakim

A2 - Outay, Fatma

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A2 - Alloum, Amira

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PB - Springer Science and Business Media Deutschland GmbH

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ER -

Fournier-Viger P, He G, Zhou M, Nouioua M, Liu J. Discovering Alarm Correlation Rules for Network Fault Management. In Hacid H, Outay F, Paik H, Alloum A, Petrocchi M, Bouadjenek MR, Beheshti A, Liu X, Maaradji A, editors, Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 228-239. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-76352-7_24

Discovering Alarm Correlation Rules for Network Fault Management

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this