Paper Title
Enabling Proactive Self-Healing by Data Mining Network Failure Logs

Abstract
Self-healing is a key desirable feature in emerging communication networks. While legacy self-healing mechanisms that are reactive in nature can minimize recovery time substan- tially, the recently conceived extremely low latency and high Quality of Experience (QoE) requirements call for self-healing mechanisms that are pro-active instead of reactive thereby enabling minimal recovery times. A corner stone in enabling proactive self-healing is predictive analytics of historical network failure logs (NFL). In current networks NFL data remains mostly dark, i.e., though they are stored but they are not exploited to their full potential. In this paper, we present a case study that investigates spatio-temporal trends in a large NFL database of a nationwide broadband operator. To discover hidden patterns in the data we leverage five different unsupervised pattern recognition and clustering along with density based outlier detection techniques namely: K-means clustering, Fuzzy C-means clustering, Local Outlier Factor, Local Outlier Probabilities and Kohonenís Self Organizing Maps. Results indicate that self- organizing maps with local outlier probabilities outperform K- means and Fuzzy C-means clustering in terms of sum of squared errors (SSE) and Davis Boulden index (DBI) values. Through an extensive data analysis leveraging a rich combination of the aforementioned techniques, we extract trends that can enable the operator to proactively tackle similar faults in future and improve QoE and recovery times and minimize operational costs, thereby paving the way towards proactive self-healing. Index Terms - K-means clustering, Fuzzy C-means clustering, Self Organizing Maps, Local Outlier Factor, Local Outlier Probabilities, Network Failure Log database