We reflect upon our experience in using the RIPE Atlas platform for measurement-based research. We show how in addition to credits, control checks using rate limits are in place to ensure that the platform does not get overloaded with measurements. We show how the Autonomous System (AS)-based distribution of RIPE Atlas probes is heavily skewed which limits possibilities of measurements sourced from a specific origin-AS. We discuss the significance of probe calibration and how we leverage it to identify load issues in older hardware versions (38.6% overall as of Sep 2014) of probes. We show how performance measurement platforms (such as RIPE Atlas, SamKnows, BISmark and Dasu) can benefit from each other by demonstrating two example use-cases. We also open discussion on how RIPE Atlas deployment can be made more useful by relaying more probe metadata information back to the scientific community and by strategically deploying probes to reduce the inherent sampling bias embedded in probe-based measurement platforms.

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This paper presents Webget, a measurement tool that measures web Quality of Service (QoS) metrics including the DNS lookup time, time to first byte (TTFB) and the download time. Webget also captures web complexity metrics such as the number and the size of objects that make up the website. We deploy the Webget test to measure the web performance of Google, YouTube, and Facebook from 182 SamKnows probes. Using a 3.5year-long (Jan 2014-Jul 2017) dataset, we show that the DNS lookup time of these popular Content Delivery Networks (CDNs) and the download time of Google have improved over time. We also show that the TTFB towards Facebook exhibits worse performance than the Google CDN. Moreover, we show that the number and the size of objects are not the only factors that affect the web download time. We observe that these webpages perform differently across regions and service providers. We also developed a web measurement system, WePR (Web Performance and Rendering) that measures the same web QoS and complexity metrics as Webget, but it also captures the web Quality of Experience (QoE) metrics such as rendering time. WePR has a distributed architecture where the component that measures the web QoS and complexity metrics is deployed on the SamKnows probe, while the rendering time is calculated on a central server. We measured the rendering performance of four websites. We show that in 80% of the cases, the rendering time of the websites is faster than the downloading time. The source code of the WePR system and the dataset is made publicly available.

Recent research has shown that last-mile latency is a key network performance indicator that contributes heavily to DNS lookup and page load times. Using a month-long dataset collected from 696 residential RIPE Atlas probes and 1245 SamKnows probes, we measure last-mile latencies from 19 ISPs (RIPE Atlas) in the US and the EU, and 9 ISPs (SamKnows) in the UK. We show that DSL deployments not only tend to enable interleaving on the last-mile, but also employ multiple depth levels that change over time. We also witness that last-mile latency is considerably stable over time and not affected by diurnal load patterns. Unlike observations from prior studies, we show that cable providers in the US do not generally exhibit lower last-mile latencies when compared to that of DSL. We instead identify that last-mile latencies vary by subscriber location and show that last-mile latencies of cable providers in the US are considerably different across the US east and west coast. We further show that last-mile latencies vary depending on the access technology used by the DSL modem wherein VDSL deployments show last-mile latencies lower than ADSL1/ADSL2+ broadband speeds. The entire dataset and software used in this study is made available to the measurement community.

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