DeepSIP: A System for Predicting Service Impact of Network Failure by Temporal Multimodal CNN
This work addresses network operators' need to quickly assess failure impacts for SLA management, though it is incremental as it builds on existing neural network approaches.
The paper tackles predicting service impact from network failures by developing DeepSIP, a system that uses a temporal multimodal CNN to forecast time to recovery and traffic loss, reducing prediction error by about 50% compared to other neural network methods.
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.