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%.

Approximate Query Processing (AQP) systems produce estimation of query answers using small random samples. It is attractive for the users who are willing to trade accuracy for low query latency. On the other hand, real-world data are often subject to concurrent updates. If the user wants to perform real-time approximate data analysis, the AQP system must support concurrent updates and sampling. Towards that, we recently developed a new concurrent index, AB-tree, to support efficient sampling under updates. In this work, we will demonstrate the feasibility of supporting realtime approximate data analysis in online transaction settings using index-assisted sampling.