Operating large-scale anycast networks is challenging because client-to-site mappings often misalign with operator's expectation due to opaque inter-domain routing. We present AnyPro, the first system to unlock the full potential of AS-path prepending (ASPP), efficiently deriving globally optimal configurations to steer clients toward performance-optimal sites at scale. AnyPro first employs an efficient polling mechanism to identify all clients sensitive to ASPP. By analyzing the routing changes during the process, the system derives a set of ASPP constraints that guide client traffic toward the desired sites. We then formulate the anycast optimization problem as a constraint-based program and compute optimal ASPP configurations. Extensive evaluation on a global testbed with 20 PoPs demonstrates the effectiveness of AnyPro: it reduces the 90th percentile latency by 37.7% compared to baseline configurations without ASPP. Furthermore, we show that AnyPro can be integrated with PoP-level anycast optimization techniques to achieve additional performance gains.

翻译：运营大规模任播网络极具挑战性，由于不透明的域间路由机制，客户端到站点的映射往往与运营商的预期存在偏差。我们提出AnyPro——首个能够充分释放AS路径预置（ASPP）潜力的系统，可高效推导全局最优配置以大规模引导客户端连接至性能最优站点。AnyPro首先采用高效轮询机制识别所有对ASPP敏感的客户端，通过分析路由变更过程推导出一组ASPP约束条件，从而将客户端流量导向目标站点。随后我们将任播优化问题建模为约束满足程序，并计算最优ASPP配置。基于包含20个PoP的全球测试平台进行的大规模评估验证了AnyPro的有效性：与未使用ASPP的基准配置相比，第90百分位延迟降低了37.7%。此外，我们证明AnyPro可与PoP级任播优化技术集成，实现进一步的性能增益。