  When a failure occurs in a network, network operators need to recognize
service impact, since service impact is essential information for handling
failures. In this paper, we propose Deep learning based Service Impact
Prediction (DeepSIP), a system to predict the time to recovery from the failure
and the loss of traffic volume due to the failure in a network element using a
temporal multimodal convolutional neural network (CNN). Since the time to
recovery is useful information for a service level agreement (SLA) and the loss
of traffic volume is directly related to the severity of the failures, we
regard these as the service impact. The service impact is challenging to
predict, since a network element does not explicitly contain any information
about the service impact. Thus, we aim to predict the service impact from
syslog messages and traffic volume by extracting hidden information about
failures. To extract useful features for prediction from syslog messages and
traffic volume which are multimodal and strongly correlated, and have temporal
dependencies, we use temporal multimodal CNN. We experimentally evaluated
DeepSIP and DeepSIP reduced prediction error by approximately 50% in comparison
with other NN-based methods with a synthetic dataset.
