This study takes a close look at mobile web performance. The two main parameters determining web page load time are the network speed and the computing power of the end-user device. Based on data from real users, this paper quantifies the relative importance of network and device. The findings suggest that increased processing power of latest generation smart phones and optimized browsers have a significant impact on web performance; up to 56 reduction in median page load time from one generation to the following. The cellular networks, on the other hand, have become so mature that the median page load time on one fiber-to-the-home network (using wifi for the last meter) is only 18-28 faster than cellular and the median page load time on one DSL network is 19 slower compared to a well-deployed cellular network.
@cite_0: Web page load time is a key performance metric that many techniques aim to reduce. Unfortunately, the complexity of modern Web pages makes it difficult to identify performance bottlenecks. We present WProf, a lightweight in-browser profiler that produces a detailed dependency graph of the activities that make up a page load. WProf is based on a model we developed to capture the constraints between network load, page parsing, JavaScript CSS evaluation, and rendering activity in popular browsers. We combine WProf reports with critical path analysis to study the page load time of 350 Web pages under a variety of settings including the use of end-host caching, SPDY instead of HTTP, and the mod pagespeed server extension. We find that computation is a significant factor that makes up as much as 35 of the critical path, and that synchronous JavaScript plays a significant role in page load time by blocking HTML parsing. Caching reduces page load time, but the reduction is not proportional to the number of cached objects, because most object loads are not on the critical path. SPDY reduces page load time only for networks with high RTTs and mod pagespeed helps little on an average page.
@cite_5: 
@cite_2: Mobile Internet availability, performance and reliability have remained stubbornly opaque since the rise of cellular data access. Conducting network measurements can give us insight into user-perceived network conditions, but doing so requires careful consideration of device state and efficient use of scarce resources. Existing approaches address these concerns in ad-hoc ways. In this work we propose Mobilyzer, a platform for conducting mobile network measurement experiments in a principled manner. Our system is designed around three key principles: network measurements from mobile devices require tightly controlled access to the network interface to provide isolation; these measurements can be performed efficiently using a global view of available device resources and experiments; and distributing the platform as a library to existing apps provides the incentives and low barrier to adoption necessary for large-scale deployments. We describe our current design and implementation, and illustrate how it provides measurement isolation for applications, efficiently manages measurement experiments and enables a new class of experiments for the mobile environment.
The advanatages of collecting performance data from real users with the Navigation Timing API as opposed to perform very limited tests from only a few vantage points are outlined in @cite_5 . The author discusses the various metrics provided by the API and shows an example on how to gain insights into the performance experienced by group of users by analyzing the collected data. Wprof @cite_0 is a tool that analyzes the dependencies of objects on a website and computes the critical path of the page load time. The authors ran tests for popular websites from their campus network using one machine (3 Ghz clock speed) and find that for their measurement setup computing time accounts for 35 very recent study @cite_2 (published after the data collection for this paper had been completed) presents a platform to run mobile network measurement experiments. It presents crowd-sourced data collected via the Navigation Timing API by 80 users and conclude that web performance can significantly benefit from faster processors. Unlike our study the effect of the network performance is not considered, in fact even wifi and cellular networks are mixed together in the results.