  Failures in optical network backbone can lead to major disruption of internet
data traffic. Hence, minimizing such failures is of paramount importance for
the network operators. Even better, if the network failures can be predicted
and preventive steps can be taken in advance to avoid any disruption in
traffic. Various data driven and machine learning techniques have been proposed
in literature for failure prediction. Most of these techniques need real time
data from the networks and also need different monitors to measure key optical
parameters. This means provision for failure prediction has to be available in
network nodes, e.g., routers and network management systems. However, sometimes
deployed networks do not have failure prediction built into their initial
design but subsequently need arises for such mechanisms. For such systems,
there are two key challenges. Firstly, statistics of failure distribution,
data, etc., are not readily available. Secondly, major changes cannot be made
to the network nodes which are already commercially deployed. This paper
proposes a novel implementable non-intrusive failure prediction mechanism for
deployed network nodes using information from log files of those devices.
Numerical results show that the mechanism has near perfect accuracy in
predicting failures of individual network nodes.
