首页|Accelerating traffic engineering optimization for segment routing: A recommendation perspective
Accelerating traffic engineering optimization for segment routing: A recommendation perspective
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NETL
NSTL
Elsevier
Traffic engineering (TE) is important for improving network performance. Recently, segment routing (SR) has gained increasing attention in the TE field. Many segment routing traffic engineering (SR-TE) methods compute optimal routing policies by solving linear programming (LP) problems, which suffer from high computation time. Therefore, various methods have been proposed for accelerating TE optimization. However, prior methods solve individual TE optimization problems from scratch, overlooking valuable information from existing historical solutions. We argue that these data can imply the distribution of optimal solutions for solving future TE problems. In this paper, we provide a new perspective on accelerating SR-TE optimization. First, we generated and analyzed historical solutions of a widely used LP model, and revealed two key findings from the data: Flows are predominantly routed through a small subset of intermediate nodes; similar decisions can be made for some flows. Then, inspired by the findings, we propose RS4SR, the first framework to our knowledge leveraging historical solutions for SR-TE acceleration. It can significantly reduce the size of LP model by performing candidate recommendation and flow clustering. Experiments on real-world topologies and various traffic matrices demonstrate that a simple implementation of RS4SR is sufficient to obtain near-optimal solutions within the time limit of two seconds on large-scale networks, utilizing a small number of historical solutions.
NETL
NSTL
Elsevier
