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With a growing demand for quasi-instantaneous communication services such asreal-time video streaming, cloud gaming, and industry 4.0 applications,multi-constraint Traffic Engineering (TE) becomes increasingly important. Whilelegacy TE management planes have proven laborious to deploy, Segment Routing(SR) drastically eases the deployment of TE paths and is thus increasinglyadopted by Internet Service Providers (ISP). There is a clear need in computingand deploying Delay-Constrained Least-Cost paths (DCLC) with SR for real-timeinteractive services. However, most current DCLC solutions are not tailored forSR. They also often lack efficiency or guarantees. Similarly to approximationschemes, we argue that the challenge is to design an algorithm providing bothperformances and guarantees. However, conversely to most of these schemes, wealso consider operational constraints to provide a practical, high-performanceimplementation.  We leverage the inherent limitations of delay measurements and account forthe operational constraint added by SR to design a new algorithm, best2cop,providing guarantees and performance in all cases. Best2cop outperforms astate-of-the-art algorithm on both random and real networks of up to 1000nodes. Relying on commodity hardware with a single thread, our algorithmretrieves all non-superfluous 3-dimensional routes in only 250ms and 100msrespectively. This execution time is further reduced using multiple threads, asthe design of best2cop enables a speedup almost linear in the number of cores.Finally, we extend best2cop to deal with massive scale ISP by leveraging themulti-area partitioning of these deployments. Thanks to our new topologygenerator specifically designed to model the realistic patterns of such massiveIP networks, we show that best2cop solves DCLC-SR in approximately 1 secondeven for ISP having more than 1 routers.

Deploying Near-Optimal Delay-Constrained Paths with Segment Routing in Massive-Scale Networks