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