Leveraging Multi-Step Traffic Forecasts for Multi-Period Planning Optical Networks

2026-05-25

AI Summary

This study addresses the trade-off between spectral efficiency and service disruption in reconfigurable optical networks by proposing a multi-period network planning framework that, for the first time, integrates multi-step deep learning–based traffic forecasting with multi-period resource allocation. An encoder–decoder model predicts traffic demands over multiple future time slots, which then drives both an integer linear programming (ILP) formulation and an efficient heuristic algorithm to optimize network configuration. While the ILP yields optimal solutions, the heuristic achieves comparable performance with significantly lower computational latency. Experimental results demonstrate that the length of the prediction horizon critically influences levels of resource over-provisioning, under-provisioning, and service disruption. The proposed framework flexibly accommodates diverse operator preferences—balancing spectrum conservation against disruption tolerance—while maintaining stringent quality-of-service guarantees.

Abstract

In this work, multi-step traffic predictions are leveraged to enable multi-period planning in reconfigurable optical networks. The proposed framework aims to achieve spectrum savings by adapting the network to predicted time-varying conditions while ensuring the necessary quality-of-service (QoS) levels. Since frequent network (re)configurations may lead to undesired service disruptions, traffic predictions spanning various prediction horizons are exploited to balance the trade-off between spectrum savings and service disruptions. For multi-step-ahead prediction, an encoder-decoder deep learning model is employed to analyze real traffic traces. Subsequently, an Integer Linear Programming (ILP) formulation and heuristic algorithms are developed that use the predictions to proactively (re)optimize future network configurations, enhancing spectrum efficiency while minimizing service disruptions. The approaches are utilized under different scenarios, with the ILP achieving better solutions overall, and the heuristics achieving solutions close to the ILP at significantly lower running times. Further, the results present the effect of the prediction horizon on disruptions and over- and under- provisioning, showcasing that the prediction horizon selection greatly depends on the network operator targets in both network performance and predefined service level agreements.

Problem

• multi-step traffic forecasting
• multi-period planning
• reconfigurable optical networks
• spectrum efficiency
• service disruption

Innovation

• multi-step traffic forecasting
• multi-period network planning
• reconfigurable optical networks
• spectrum efficiency
• service disruption minimization