Make optical transmission quality prediction more effective: A parallel computing view

In optical networks, ensuring high quality of transmission (QoT) is essential to prevent degradation of optical signals, especially when the signal strength falls below a specified threshold. While machine learning (ML) is widely used for QoT prediction, predicting QoT accurately for large-scale optical links presents challenges. Traditional serial methods often result in high latency and decreased processing efficiency of optical channels. To solve this problem, this paper proposes a Dask-based P-FEDformer approach. Initially, a FEDformer-based predictor is constructed, and then QoT prediction for multiple channels is realized under the Dask parallel architecture. To enhance model prediction accuracy, wavelet decomposition technique is employed. Simulation results demonstrate the method’s effectiveness in handling large amount of data with a 60% improvement in time efficiency compared to serial execution, while maintaining accurate QoT prediction. •A new framework for combining Dask with optical QoT prediction models is proposed.•Combining FEDformer with wavelet decomposition to improve prediction accuracy.•Experiments on Microsoft’s dataset show P-FEDformer outperforms benchmarks.