Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
This work addresses network resource efficiency for optical network operators, but it appears incremental as it builds on existing prediction and planning methods.
The paper tackles the problem of service overprovisioning and adaptability in optical networks by proposing a multi-period planning framework that uses multi-step ahead traffic predictions, resulting in heuristics that outperform single-step prediction approaches in minimizing service disruptions.
A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels. An encoder-decoder deep learning model is initially leveraged for multi-step ahead prediction by analyzing real-traffic traces. This information is then exploited by multi-period planning heuristics to efficiently utilize available network resources while minimizing undesired service disruptions (caused due to lightpath re-allocations), with these heuristics outperforming a single-step ahead prediction approach.