AI‑Based TELGEN Algorithm Cuts Traffic Engineering Time While Maintaining Near‑Optimal Performance

Global: AI‑Based TELGEN Algorithm Cuts Traffic Engineering Time While Maintaining Near‑Optimal Performance

Researchers have introduced TELGEN, an artificial intelligence–based traffic engineering (TE) algorithm designed to handle the scalability demands of extensive networks such as cloud wide‑area networks and low‑earth‑orbit satellite constellations. The study reports that TELGEN learns to approximate optimal TE solutions while maintaining feasibility across a broad range of network topologies and traffic patterns.

Methodological Innovation

Instead of directly predicting the optimal TE configuration, TELGEN reframes the task as predicting the optimal TE algorithm itself. This approach enables the model to internalize the end‑to‑end solving process of classical optimal TE methods, making the learned algorithm independent of specific network structures or demand profiles.

Training and Evaluation Scope

The authors trained TELGEN on a mixture of synthetic and real‑world topologies, scaling the evaluation to networks comprising up to 5,000 nodes and 3.6 × 10⁶ links. Testing included scenarios where the network size exceeded the training maximum by a factor of 2 to 20.

Performance Metrics

Across all test cases, TELGEN maintained an optimality gap of less than 3 % while guaranteeing feasible solutions. Compared with traditional interior‑point methods, the algorithm reduced TE solving time by up to 84 %. In addition, per‑epoch training time was lowered by as much as 79.6 % relative to the leading learning‑based baseline.

Implications for Large‑Scale Deployments

These results suggest that TELGEN could provide a more efficient and adaptable alternative to conventional TE techniques, particularly in environments where rapid reconfiguration and scalability are critical. The algorithm’s topology‑agnostic design may simplify deployment across heterogeneous network infrastructures.

Future Directions

The authors note that further research will explore integration with real‑time network monitoring systems and assess robustness under dynamic traffic fluctuations.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.