LSTM-Based Traffic Forecasting for Dynamic P2MP Light-Tree Reconfiguration in Metro-Access Networks

Abstract

Point-to-multipoint (P2MP) coherent optical architectures that use digital subcarrier multiplexing support efficient aggregation in metro-access networks. Proactive provisioning of hub capacity requires accurate per-spoke demand forecasts. However, spoke nodes carry heterogeneous traffic profiles such as business, residential, and mixed, with distinct diurnal patterns and forecast difficulty. We examine whether a single multivariate Long Short-Term Memory (LSTM) network can capture these profile-specific dynamics without explicit labels, and quantify how the resulting per-profile forecast accuracy propagates to operational P2MP resource provisioning metrics. The forecasts feed a greedy reconfiguration-aware heuristic algorithm, which jointly decides light-tree assignment at each control epoch while accounting for rerouting, resizing, and point-to-point forwarding penalties. Evaluation on a 30-node TID-derived metro topology with 300 spokes with different OSNR budgets shows that the joint LSTM reduces per-profile mean absolute error by 13-22% over a seasonal baseline, with the largest gains on business spokes. LSTM-driven provisioning achieves 13-15% lower total operational cost than seasonal-driven provisioning and 29-30% savings over static peak allocation, while maintaining demand violations below 1.2%. The LSTM reduces reconfiguration churn by 12–26% per profile over seasonal and lagged baselines, largest on mixed spokes whose composite weekday/weekend pattern is hardest for seasonal forecasts to track.