Improving Scalability in Traffic Engineering via Optical Topology Programming

We present a novel framework, GreyLambda, to improve the scalability of traffic engineering (TE) systems. TE systems continuously monitor traffic and allocate network resources based on observed demands. The temporal requirement for TE is to have a time-to-solution in five minutes or less. Additionally, traffic allocations have a spatial requirement, which is to enable all traffic to traverse the network without encountering an over-subscribed link. However, the multi-commodity flowbased TE formulation cannot scale with increasing network sizes. Recent approaches have relaxed multi-commodity flow constraints to meet the temporal requirement but fail to satisfy the spatial requirement due to changing traffic demands, resulting in oversubscribed links or infeasible solutions. To satisfy both these requirements, we utilize optical topology programming (OTP) to rapidly reconfigure optical wavelengths in critical network paths and provide localized bandwidth scaling and new paths for traffic forwarding. GreyLambda integrates OTP into TE systems by introducing a heuristic algorithm that capitalizes on latent hardware resources at high-degree nodes to offer bandwidth scaling, and a method to reduce optical path reconfiguration latencies. Our experiments show that GreyLambda enhances the performance of two state-of-the-art TE systems, SMORE and NCFlow in real-world topologies with challenging traffic and link failure scenarios.