AnyTuner: Optimizing IP Anycast Performance via Strategic BGP Routing Policy
M. B. Zhou, Jiaqi Zheng, Congying Wang, Guihai Chen, Wanchun Dou · 2025
Optimizing anycast catchment to ensure clients connect to their expected PoPs remains a significant challenge in production networks, primarily due to BGP’s inherent lack of performance awareness. Existing approaches either lack fine-grained routing policy control or prove operationally impractical. In this paper, we present AnyTuner, an automatic route management system that strategically optimizes anycast performance through AS-path prepending and BGP communities. AnyTuner integrates three key components: a measurement-driven optimization loop, a neural network for PoP traffic matrix prediction, and an automated recommendation engine for configuration generation. Deployed for over two years on a commercial cloud platform, AnyTuner demonstrates remarkable efficacy in our evaluation across 18 PoPs and 9,000+ probes, achieving a 39.9% reduction in median RTT (from 36.3 ms to 21.8 ms) compared to policy-free baseline configurations and outperforming state-of-the-art alternatives like AnyOpt by 27.8%.