Telecom Alarm Prioritization Using Neural Networks

Telecom Service Providers are faced with an overwhelming flow of alarms. Network administrators need to judge which alarms to resolve in order to maintain the service quality. The problem is that it is hard to pick the most important alarms. Which alarms have the highest priority? A solution that automatically assigns priorities to alarms would increase the efficiency of Network Management Centers. We have prototyped a solution that uses neural networks to assign alarm priority. The neural network learns from network administrators by using the manually assigned priorities in trouble-tickets. Our tests are based on live-data from a large mobile service provider and we show that neural networks can learn to assign relevant priorities to 75% of the alarms.