Detecting and predicting outages in mobile networks with log data.

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.