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

• URL: http://arxiv.org/abs/2404.08314v1
• Date: Fri, 12 Apr 2024 08:20:01 GMT
• Title: Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
• Authors: Hafsa Maryam, Tania Panayiotou, Georgios Ellinas,
• Abstract summary: A multi-period planning framework is proposed that exploits multi-step traffic predictions to address service overprovisioning and improve adaptability to traffic changes. An encoder-decoder deep learning model is initially leveraged for multi-step ahead prediction by analyzing real-traffic traces.
• Score: 4.963536645449425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 single-step ahead prediction approach.

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