Rethinking network management: models, data-mining and self-learning
2012 (English)In: Proceedings of the 2012 IEEE Network Operations and Management Symposium: Maui, HI 16- 20 April 2012, Piscataway, NJ: IEEE Communications Society, 2012, 880-886 p.Conference paper (Refereed)
Network Service Providers are struggling to re- duce cost and still improve customer satisfaction. We have looked at three underlying challenges to achieve these goals; an overwhelming flow of low-quality alarms, understanding the structure and quality of the delivered services, and automation of service configuration. This thesis proposes solutions in these areas based on domain-specific languages, data-mining and self- learning. Most of the solutions have been validated based on data from a large service provider.We look at how domain-models can be used to capture explicit knowledge for alarms and services. In addition, we apply data- mining and self-learning techniques to capture tacit knowledge. The validation shows that models improve the quality of alarm and service models, and automatically render functions like root cause correlation, service and SLA status, as well as service configuration automation.The data-mining and self-learning solutions show that we can learn from available decisions made by experts and automatically assign alarm priorities.
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2012. 880-886 p.
, I E E E - I F I P Network Operations and Management Symposium, ISSN 1542-1201
Research subject Dependable Communication and Computation Systems; Mobile and Pervasive Computing
IdentifiersURN: urn:nbn:se:ltu:diva-29298DOI: 10.1109/NOMS.2012.6212003Local ID: 2b9e6f6e-7e84-4edb-b7bb-979ccd10f5e8ISBN: 978-1-4673-0267-8ISBN: 978-1-4673-0268-5 (PDF)OAI: oai:DiVA.org:ltu-29298DiVA: diva2:1002521
IEEE/IFIP Network Operations and Management Symposium : 16/04/2012 - 19/04/2012
Godkänd; 2012; 20111223 (stewal)2016-09-302016-09-30Bibliographically approved