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 Kohonen’s 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
Author - Panthagani Vijaya Babu, Jajjara Bhargav Ramudu, Narne Srikanth
Published : Volume-4,Issue-4 ( Apr, 2017 )
DOIONLINE Number - IJAECS-IRAJ-DOIONLINE-7687
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Published on 2017-06-23 |
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