AnyTuner: Optimizing IP Anycast Performance via Strategic BGP Routing Policy

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%.

Authors

Institutions

Publication Details

Published
2025-09-22
DOI
https://doi.org/10.1109/icnp65844.2025.11192428
Citations
1
Primary Topic
Network Traffic and Congestion Control
Type
article
Field-Weighted Citation Impact
1.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AnyTuner: Optimizing IP Anycast Performance via Strategic BGP Routing Policy

Congying Wang, Wanchun Dou, M. B. Zhou, Guihai Chen et al.
1 citations
Network Traffic and Congestion Control
1.49
article

AnyTuner: Optimizing IP Anycast Performance via Strategic BGP Routing Policy

Congying Wang, Wanchun Dou, M. B. Zhou, Guihai Chen, Jiaqi Zheng
article en
1 citations

Abstract

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%.

Nanjing University (CN)
Openalex Percentile: Top 12%
Network Traffic and Congestion Control
1.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AnyTuner: Optimizing IP Anycast Performance via Strategic BGP Routing Policy — Congying Wang, Wanchun Dou, et al. · (2025) | TGRS Research Map | TGRS