Automatic Detection of Mass Outages in Radio Access Networks

Fault management in mobile networks is required for detecting, analysing, and fixing problems appearing in the mobile network. When a large problem appears in the mobile network, multiple alarms are generated from the network elements. Traditionally Network Operations Center (NOC) process the reported failures, create trouble tickets for problems, and perform a root cause analysis. However, alarms do not reveal the root cause of the failure, and the correlation of alarms is often complicated to determine. If the network operator can correlate alarms and manage clustered groups of alarms instead of separate ones, it saves costs, preserves the availability of the mobile network, and improves the quality of service. Operators may have several electricity providers and the network topology is not correlated with the electricity topology. Additionally, network sites and other network elements are not evenly distributed across the network. Hence, we investigate the suitability of a density-based clustering methods to detect mass outages and perform alarm correlation to reduce the amount of created trouble tickets. This thesis focuses on assisting the root cause analysis and detecting correlated power and transmission failures in the mobile network. We implement a Mass Outage Detection Service and form a custom density-based algorithm. Our service performs alarm correlation and creates clusters of possible power and transmission mass outage alarms. We have filed a patent application based on the work done in this thesis. Our results show that we are able to detect mass outages in real time from the data streams. The results also show that detected clusters reduce the number of created trouble tickets and help reduce of the costs of running the network. The number of trouble tickets decreases by 4.7-9.3% for the alarms we process in the service in the tested networks. When we consider only alarms included in the mass outage groups, the reduction is over 75%. Therefore continuing to use, test, and develop implemented Mass Outage Detection Service is beneficial for operators and automated NOC