In this paper, we perform the first large-scale study of how people spend time on the web. Our study is based on anonymous, aggregate telemetry data from several hundred million Google Chrome users who have explicitly enabled sharing URLs with Google and who have usage statistic reporting enabled. We analyze the distribution of web traffic, the types of websites that people visit and spend the most time on, the differences between desktop and mobile browsing behavior, the geographical differences in web usage, and the most popular websites in regions worldwide. Our study sheds light on online user behavior and how the research community can more accurately analyze the web in the future.

The last decade has seen power consumption move from an afterthought to the foremost design constraint of new supercomputers. Measuring the power of a supercomputer can be a daunting proposition, and as a result, many published measurements are extrapolated. This paper explores the validity of these extrapolations in the context of inter-node power variability and power variations over time within a run. We characterize power variability across nodes in systems at eight supercomputer centers across the globe. This characterization shows that the current requirement for measurements submitted to the Green500 and others is insufficient, allowing variations of up to 20% due to measurement timing and a further 10--15% due to insufficient sample sizes. This paper proposes new power and energy measurement requirements for supercomputers, some of which have been accepted for use by the Green500 and Top500, to ensure consistent accuracy.