Separating mid-path Internet performance from edge effects remains a fundamental challenge in network measurement. This paper presents a methodology for detecting anomalous topology, routing policies, and congested interconnections using controlled A/B comparisons derived from Measurement Lab (M-Lab) data. The approach leverages M-Lab's uniform server selection policy: by comparing performance distributions from clients in the same access ISP to different nearby M-Lab servers, natural experiments are created that isolate mid-path effects while controlling for client-side variation, access network bottlenecks, and diurnal variation in test volume. This analysis is implemented in BigQuery using sparse multidimensional histograms enabling efficient computation of Kolmogorov-Smirnov distance and ratios of geometric mean throughput across many millions of measurements in a single pass. Differences in throughput suggest mid-path bandwidth bottlenecks or traffic management; excess differences in minimum RTT suggest suboptimal routing. These signals of interconnection problems are extracted from the noise deliberately suppressed by other measurement approaches. Public dashboards provide ongoing visibility into all M-Lab metropolitan regions with sufficient servers, with drill-down capability to individual ISP--server plots.

翻译：将互联网中间路径性能与边缘效应分离仍是网络测量领域的基础性挑战。本文提出一种基于Measurement Lab（M-Lab）数据的可控A/B对比分析方法，用于检测异常拓扑、路由策略及拥塞互联。该方法利用M-Lab统一的服务器选择策略：通过比较同一接入ISP中客户端与不同邻近M-Lab服务器间的性能分布，构建自然实验环境，在控制客户端差异、接入网瓶颈及测试量的昼夜变化的同时，分离出中间路径效应。该分析基于BigQuery实现，采用稀疏多维直方图技术，通过单次扫描即可高效计算数百万条测量的Kolmogorov-Smirnov距离及几何平均吞吐量比值。吞吐量差异暗示中间路径带宽瓶颈或流量管控，最小RTT的异常差异则指向次优路由。这些互联问题信号是从其他测量方法刻意抑制的噪声中提取的。公开仪表板持续展示所有配备充足服务器的M-Lab大都市区信息，并支持逐层钻取至具体ISP-服务器视图。