[en] Modern cellular networks are complex systems offering a wide range of services
and present challenges in detecting anomalous events when they do occur. The
networks are engineered for high reliability and, hence, the data from these
networks is predominantly normal with a small proportion being anomalous. From
an operations perspective, it is important to detect these anomalies in a timely
manner, to correct vulnerabilities in the network and preclude the occurrence of
major failure events. The objective of our work is anomaly detection in cellular
networks in near real-time to improve network performance and reliability. We
use performance data from a 4G LTE network to develop a methodology for anomaly
detection in such networks. Two rigorous prediction models are proposed: a
non-parametric approach (Chi-Square test), and a parametric one (Gaussian
Mixture Models). These models are trained to detect differences between
distributions to classify a target distribution as belonging to a normal period
or abnormal period with high accuracy. We discuss the merits between the
approaches and show that both provide a more nuanced view of the network than
simple thresh- olds of success/failure used by operators in production networks
today.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN) Nokia Bell Labs
Disciplines :
Computer science
Author, co-author :
Gurbani, Vijay K.; Nokia Bell Labs
Kushnir, Dan; Nokia Bell Labs
Mendiratta, Veena B.; Nokia Bell Labs
Phadke, Chitra; Nokia Bell Labs
Falk, Eric ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
State, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
yes
Language :
English
Title :
Detecting and predicting outages in mobile networks with log data.
Publication date :
May 2017
Event name :
IEEE International Conference on Communications, ICC 2017
Event organizer :
IEEE
Event date :
from 21-05-2017 to 25-05-2017
Audience :
International
Main work title :
IEEE International Conference on Communications, ICC 2017
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