Link load prediction in an optical network with restoration mechanisms

Knowledge about the future link load is an essential issue for optical network operators, especially in the case of network failure and the restoration of affected traffic. In this paper, we study the dynamic operation of an optical network in its normal non-failure state and the situation of a link failure followed by the restoration process. Data obtained during the simulation are used for link load prediction. We propose dynamic methods for multistep link load prediction. The analyzed link load is expressed in two ways: the bitrate of the overall traffic allocated to the link and the number of frequency slots occupied. The proposed prediction methods are expanded to include additional mechanisms that improve the forecasting quality expressed by the mean absolute percentage error metric. We evaluate developed methods on a dataset collected using a representative European network topology with realistic traffic containing diverse types of network transmissions. In broad numerical experiments, we prove the high prediction quality of regression algorithms aided by the proposed additional features.