The Internet's universality is based on its decentralization and diversity. However, its distributed nature leads to operational brittleness and difficulty in identifying the root causes of performance and availability issues, especially when the involved systems span multiple administrative domains. The first step to address this fragmentation is coordinated measurement -- we propose to complement the current Internet's data and control planes with a measurement plane, or mPlane for short. mPlane's distributed measurement infrastructure collects and analyses traffic measurements at a wide variety of scales to monitor the network status. Its architecture is centered on a flexible control interface, allowing the incorporation of existing measurement tools through lightweight mPlane proxy components, and offering dynamic support for new capabilities. A focus on automated, iterative measurement makes the platform well-suited to troubleshooting support. This is supported by a reasoning system, which applies machine learning algorithms to learn from success and failure in drilling down to the root cause of a problem. This paper describes the mPlane architecture and shows its applicability to several distributed measurement problems involving Content Delivery Networks (CDNs) and Internet Service Providers (ISPs). A first case study presents the tracking and iterative analysis of cache selection policies in Akamai, whereas a second example focuses on the cooperation between ISPs and CDNs to better orchestrate their traffic engineering decisions and jointly improve their performance.