Multi-Step Traffic Prediction for Multi-Period Planning in Optical
  Networks
Hafsa Maryam Tania Panayiotou Georgios Ellinas
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.