Taming Imbalance and Complexity in WAN Traffic Engineering
Yufeng Xin, Sajith Sasidharam, Cong Wang, Mert Cevik
TL;DR
This work tackles the imbalance and computational bottlenecks in WAN Traffic Engineering by proposing a holistic framework that combines adaptive, demand-aware tunnel provisioning with new resilience-aware metrics. The core idea is to replace uniform, large tunnel sets with an adaptive scheme, and to evaluate resilience via a network criticality metric that accounts for both demand satisfaction and link bottlenecks. Empirical results on small and large WAN topologies show that adaptive tunnels dramatically reduce computation time and improve resilience indicators, at a manageable cost to overprovisioning and unmet demands, depending on solver choice. Overall, the approach advances scalable, resilient TE for large-scale WANs by balancing efficiency, capacity utilization, and robustness to failures.
Abstract
The rapid expansion of global cloud infrastructures, coupled with the growing volume and complexity of network traffic, has fueled active research into scalable and resilient Traffic Engineering (TE) solutions for Wide Area Networks (WANs). Despite recent advancements, achieving an optimal balance between solution quality and computational complexity remains a significant challenge, especially for larger WAN topologies under dynamic traffic demands and stringent resource constraints. This paper presents empirical evidence of a critical shortcoming in existing TE solutions: their oversight inadequately accounting for traffic demand heterogeneities and link utilization imbalances. We identify key factors contributing to these issues, including traffic distribution, solver selection, resiliency, and resource overprovisioning. To address these gaps, we propose a holistic solution featuring new performance metrics and a novel resilient TE algorithm. The proposed metrics, critical link set and network criticality, provide a more comprehensive assessment of resilient TE solutions, while the tunnel-based TE algorithm dynamically adapts to changing traffic demands. Through extensive simulations on diverse WAN topologies, we demonstrate that this holistic solution significantly improves network performance, achieving a superior balance across key objectives. This work represents a significant advancement in the development of resilient and scalable TE solutions for WANs.