Alarm and service monitoring of large scale multi-service mobile

115 Pages·2009·1.17 MB·English

by  Stefan Wallin

ISSN: 1402-1544 ISBN 978-91-86233-XX-X Se i listan och fyll i siffror där kryssen är
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Luleå University of Technology

Alarm and Service Monitoring of
Large-Scale Multi-Service Mobile
Networks
Stefan Wallin
ICT
Dept. of Computer Science and Electrical Engineering
Lule˚a University of Technology
Lule˚a, Sweden
Supervisor:
Associate Professor Christer ˚Ahlund and Dr. Evgeny Osipov
Tryck: Universitetstryckeriet, Luleå
ISSN: 1402-1757
ISBN 978-91-86233-34-1
Luleå (cid:17)(cid:15)(cid:15)(cid:24)
www.ltu.se
And here he remained in such terror as none but
he can know, trembling in every limb, and the cold sweat starting from every pore, when
suddenly there rose upon the night-wind the noise of distant shouting, and the roar of
voicesmingled inalarmandwonder. Anysound ofmeninthat lonelyplace, even though
itconveyedarealcauseofalarm,wassomethingtohim. Heregainedhisstrengthanden-
ergyattheprospectofpersonaldanger;andspringingtohisfeet,rushedintotheopenair.
- Charles Dickens, Oliver Twist
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Abstract
Two of the most important challenges in network service assurance are
• an overwhelming flow of low-quality alarms
• understanding the structure and quality of the delivered services
This thesis proposes solutions for alarm and service monitoring that address monitoring
of large scale multi-service mobile networks.
Theworkonalarmsisbasedonstatisticalanalysisofdatacollectedfromareal-world
alarmflowandanassociatedtroubleticketdatabasecontainingthenetworkadministra-
tors’ expert knowledge. Using data from the trouble ticketing system as a reference, we
examine the relationship between the original alarm severity and the human perception
ofthealarmpriority. Usingthisknowledge,wesuggestaneuralnetwork-basedapproach
for alarm prioritization. Tests using live data show that our prototype assigns the same
severity as a human expert in 50% of all cases, compared to 17% for a na¨ıve approach.
In order to model and monitor the services, this thesis proposes a novel domain-
specific language called SALmon, which permits efficient representation of service mod-
els, along with a computational engine for evaluation of service models. We show that
the proposed system is a good match for real-world scenarios with special requirements
around service modeling.
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Contents
Chapter 1 – Thesis Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Chapter 2 – Towards Better Network Management Solutions 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 The Chaotic Alarms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Service Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Chapter 3 – Summary of Publications 15
3.1 Overview of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Paper A – Rethinking Network Management Solutions 25
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 Ways to Improve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Paper B – Telecom Network and Service Management: an Operator Survey 37
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3 Current Status of Network Management . . . . . . . . . . . . . . . . . . 41
4 OSS Motivation and Drivers . . . . . . . . . . . . . . . . . . . . . . . . . 41
5 The Future of OSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
6 Standards and Research Efforts . . . . . . . . . . . . . . . . . . . . . . . 45
7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Paper C – Multipurpose Models for QoS Monitoring 51
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3 Reference Architecture for a QoS Monitoring Solution . . . . . . . . . . . 57
4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 61
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PaperD–StatisticalAnalysisandPrioritizationofTelecomAlarmsusingNeural
Networks 65
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2 Defining the Alarm Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3 Data Mining Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4 Quantitative Analysis of Alarm Flow . . . . . . . . . . . . . . . . . . . . 72
5 Using Neural Networks for Prioritizing Alarms . . . . . . . . . . . . . . . 75
6 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7 Conclusion and future work . . . . . . . . . . . . . . . . . . . . . . . . . 82
Paper E – SALmon - A Service Modeling Language and Monitoring Engine 85
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
2 The Modeling Language . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3 Prototype Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4 Scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 97