English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Failures in optical network backbone can cause significant interruption ininternet data traffic. Hence, it is very important to reduce such networkoutages. Prediction of such failures would be a step forward to avoid suchdisruption of internet services for users as well as operators. Severalresearch proposals are available in the literature which are applications ofdata science and machine learning techniques. Most of the techniques rely onsignificant amount of real time data collection. Network devices are assumed tobe equipped to collect data and these are then analysed by different algorithmsto predict failures. Every network element which is already deployed in thefield may not have these data gathering or analysis techniques designed intothem initially. However, such mechanisms become necessary later when they arealready deployed in the field. This paper proposes a Bayesian network basedfailure prediction of network nodes, e.g., routers etc., using very basicinformation from the log files of the devices and applying power law based dataaugmentation to complement for scarce real time information. Numerical resultsshow that network node failure prediction can be performed with high accuracyusing the proposed mechanism.

A Data Augmented Bayesian Network for Node Failure Prediction in Optical Networks