AnyPro：基于策略的AS路径前置的偏好保持任意cast优化
AnyPro: Preference-Preserving Anycast Optimization based on Strategic AS-Path Prepending
AI总结：
AnyPro通过高效机制识别敏感客户端并制定AS路径前置约束，实现大规模任意cast网络的性能优化，降低90百分位延迟37.7%。
摘要：
在大规模任意cast网络中，客户端到站点的映射常与运营商预期不一致，由于域间路由不透明。我们提出了AnyPro，首个能够解锁AS路径前置（ASPP）全部潜力的系统，高效推导出全局最优配置，使客户端流量导向性能最优的站点。AnyPro首先采用高效轮询机制识别所有对ASPP敏感的客户端。通过分析过程中路由变化，系统推导出一组ASPP约束，引导客户端流量至目标站点。我们然后将任意cast优化问题表述为基于约束的程序，并计算最优ASPP配置。在包含20个PoP的全球测试床上的广泛评估显示，AnyPro相比无ASPP的基线配置，将90百分位延迟降低了37.7%。此外，我们还展示了AnyPro可以与PoP级任意cast优化技术集成，以实现额外的性能提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
