Retentive Time Series: A Scalable Machine Learning Model for Traffic Prediction in Elastic Optical Networks
Year: 2025
Type: article
Abstract: Accurate traffic prediction is crucial for elastic optical networks (EONs) as it improves network scalability, performance, and operational efficiency. High accuracy is fundamental for performing long-term traffic management and network planning, while time-sensitive predictions allow efficient real-time adaptability. In this study, we propose a new RNN-based model, retentive time series (Ret-TS), which overcomes some of the limitations of traditional RNNs in EONs scenarios. Compared to RNNs, which successively consider data and always suffer from problematic vanishing gradients, Ret-TS can handle long-term dependencies efficiently through parallel computation; hence, it is more suitable for large-scale and real-time applications. In contrast to the Transformer models, Ret-TS is computationally more efficient, with a lower time and memory complexity of O(L), making it more suitable for resource-limited devices. This research demonstrates that Ret-TS is robust for both short- and long-term traffic forecasting with better prediction accuracy and computational efficiency than traditional models based on RNNs and Transformers. Extensive simulations performed on traffic datasets have shown that Ret-TS reduces prediction errors and network blocking probabilities under different traffic loads in three network topologies: NSFNET, Janos-US, and US100. The results confirm Ret-TS’s robustness and scalability, making it an effective solution for real-world applications in modern optical networks, including both telecommunications and data center networks.
Source: IEEE Access
Authors: Faranak Khosravi, Mehdi Shadaram
Institution: The University of Texas at San Antonio
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4411949015