Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
arxiv(2024)
摘要
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.
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