预处理你的路径——加速基于线性规划的段路由流量工程优化
Preprocess your Paths -- Speeding up Linear Programming-based Optimization for Segment Routing Traffic Engineering
AI总结：
本文综述了现有的段路由预处理方法，提出了一种组合预处理技术，在不显著降低解质量的前提下，将基于线性规划的流量工程计算时间减少约10倍以上，大幅提升了大型网络中的实用性。
摘要：
许多最先进的段路由（SR）流量工程（TE）算法依赖于线性规划（LP）优化。然而，后者较差的可扩展性以及由此产生的高计算时间，严重限制了此类方法在许多用例中的实际可用性。为了解决这个问题，人们提出了多种预处理方法，旨在通过预先限制优化期间考虑的SR路径数量来降低计算复杂度。在本文中，我们首次对现有的SR预处理方法进行了广泛的文献综述。基于此，我们使用各种真实世界的拓扑结构进行了大规模的比较研究，包括来自Tier-1互联网服务提供商（ISP）骨干网的最新数据。基于从该评估中获得的见解，我们最终提出了多种预处理方法的组合，并表明这可以可靠地将计算时间减少约10倍或更多，而不会导致解决方案质量的显著恶化。这是对当前技术水平的重大改进，并促进了基于LP的优化在大型段路由网络中的可靠可用性。
英文摘要：
Many state-of-the-art Segment Routing (SR) Traffic Engineering (TE) algorithms rely on Linear Program (LP)-based optimization. However, the poor scalability of the latter and the resulting high computation times impose severe restrictions on the practical usability of such approaches for many use cases. To tackle this problem, a variety of preprocessing approaches have been proposed that aim to reduce computational complexity by preemtively limiting the number of SR paths to consider during optimization. In this paper, we provide the first extensive literature review of existing preprocessing approaches for SR. Based on this, we conduct a large scale comparative study using various real-world topologies, including recent data from a Tier-1 Internet Service Provider (ISP) backbone. Based on the insights obtained from this evaluation, we finally propose a combination of multiple preprocessing approaches and show that this can reliably reduce computation times by around a factor of 10 or more, without resulting in relevant deterioration of the solution quality. This is a major improvement over the current state-of-the-art and facilitates the reliable usability of LP-based optimization for large segment-routed networks.
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