🤖 AI Summary
This work addresses the performance degradation in large-scale anycast networks caused by opaque inter-domain routing, which often steers clients to suboptimal sites. It presents the first systematic exploration of Autonomous System (AS) path prepending (ASPP) for anycast optimization. By actively probing the network to identify ASPP-sensitive clients, the authors formulate an ASPP-aware constraint model and cast it as a constrained optimization problem to compute globally optimal traffic steering strategies that balance operator preferences with performance objectives. Evaluated on a global testbed spanning 20 points of presence (PoPs), the proposed approach reduces the 90th-percentile latency by 37.7% compared to a baseline without ASPP, while remaining complementary to existing site-level optimization techniques.
📝 Abstract
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.