Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks

arXiv:2404.08314v1 Announce Type: cross
Abstract: 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 …

abstract adaptability arxiv cs.lg cs.ni decoder deep learning encoder encoder-decoder exploits framework networks optical planning prediction predictions quality service traces traffic type