Web-based speed tests are popular among end-users for measuring their network performance. Thousands of measurement servers have been deployed in diverse geographical and network locations to serve users worldwide. However, most speed tests have opaque methodologies, which makes it difficult for researchers to interpret their highly aggregated test results, let alone leverage them for various studies. In this paper, we propose WebTestKit, a unified and configurable framework for facilitating automatic test execution and cross-layer analysis of test results for five major web-based speed test platforms. Only capturing packet headers of traffic traces, WebTestKit performs in-depth analysis by carefully extracting HTTP and timing information from test runs. Our testbed experiments showed WebTestKit is lightweight and accurate in interpreting encrypted measurement traffic. We applied WebTestKit to compare the use of HTTP requests across speed tests and investigate the root causes for impeding the accuracy of latency measurements, which play an important role in test server selection and throughput estimation.

Public cloud platforms are vital in supporting online applications for remote learning and telecommuting during the COVID-19 pandemic. The network performance between cloud regions and access networks directly impacts application performance and users' quality of experience (QoE). However, the location and network connectivity of vantage points often limits the visibility of edge-based measurement platforms (e.g., RIPE Atlas).

We designed and implemented the CLoud-based Applications Speed Platform (CLASP) to measure performance to various networks from virtual machines in cloud regions with speed test servers that have been widely deployed on the Internet. In our five-month longitudinal measurements in Google Cloud Platform (GCP), we found that 30-70% of ISPs we measured showed severe throughput degradation from the peak throughput of the day.