By Topic

Rethinking network management: Models, data-mining and self-learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wallin, S. ; Lulea Univ. of Technol., Lulea, Sweden ; Ahlund, C. ; Nordlander, J.

Network Service Providers are struggling to reduce 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 enables automatic rendering of functions like root cause correlation, service and SLA status, as well as service configuration. The data-mining and self-learning solutions show that we can learn from available decisions made by experts and automatically assign alarm priorities.

Published in:

Network Operations and Management Symposium (NOMS), 2012 IEEE

Date of Conference:

16-20 April 2012