Optimizing network operation through NW data analytics

Recently, sudden traffic spikes due to cyber attacks, flash crowds, and heavy content delivery traffic have often caused unpredictable congestion. Moreover, the congestion in large-scale networks can result in service outages that inflict a lot of damage on customers. To provide quality network service without such network anomalies, it is required to analyze network data, proactively detect the anomalies, and find root causes. We propose two technologies for performing such proactive network operation; 1) network fault diagnosis technology based on network log tensor factorization (LTF), and 2) predictive-based traffic engineering, i.e., traffic-route control technologies using the prediction of traffic variations based on the diagnosis. We examine these technologies through actual network data.

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