Web page performance is crucial in today's Internet ecosystem, and Web developers use various developer tools to analyze their page load performance. However, existing tools cannot be used to identify the critical bottlenecks during the page load process. In this work, we design an online tool called WProfX that allows Web developers to visually identify bottlenecks in their page structure. The key to WProfX is that unlike existing Web performance tools, WProfX not only visualizes the page load activity timings, but also extracts the dependencies between the activities. Using the dependency structure, WProfX identifies the critical bottleneck activities. This lets a developer quickly identify why their page is loading slow and conduct what-if analyses to study the effect of different optimizations. WProfX uses low-level tracing information exposed by most major browsers to extract the relationship between page load activities. The result is that WProfX works with most major browsers and newer browser versions. WProfX visualizes the page load process as a dependency graph of semantically meaningful Web activities and identifies the critical bottlenecks. We evaluate WProfX with 14 Web developers who perform three what-if analysis tasks involving identifying the page load bottleneck and evaluating the effect of a page optimization. All the participants were able to complete the tasks with WProfX, compared to less than 60% when using the popular developer tools available today. WProfX is currently being used by Web developers in a large telecom and at a Silicon Valley startup.

Given the growing significance of network performance, it is crucial to examine how to make the most of available network options and protocols. We propose ECON, a model that predicts performance of applications under different protocols and network conditions to scalably make better network choices. ECON is built on an analytical framework to predict TCP performance, and uses the TCP model as a building block for predicting application performance. ECON infers a relationship between loss and congestion using empirical data that drives an online model to predict TCP performance. ECON then builds on the TCP model to predict latency and HTTP performance. Across four wired and one wireless network, our model outperforms seven alternative TCP models. We demonstrate how ECON (i) can be used by a Web server application to choose between HTTP/1.1 and HTTP/2 for a given Web page and network condition, and (ii) can be used by a video application to choose the optimal bitrate that maximizes video quality without rebuffering.

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Mobile page load times are an order of magnitude slower compared to non-mobile pages. It is not clear what causes the poor performance: the slower network, the slower computational speeds, or other reasons. Further, most Web optimizations are designed for non-mobile browsers and do not translate well to the mobile browser. Towards understanding mobile Web page load times, in this paper we: (1) perform an in-depth pairwise comparison of loading a page on a mobile versus a non-mobile browser, and (2) characterize the bottlenecks in the mobile browser {\em vis-a-vis} non-mobile browsers. To this end, we build a testbed that allows us to directly compare the low-level page load activities and bottlenecks when loading a page on a mobile versus a non-mobile browser. We find that computation is the main bottleneck when loading a page on mobile browsers. This is in contrast to non-mobile browsers where networking is the main bottleneck. We also find that the composition of the critical path during page load is different when loading pages on the mobile versus the non-mobile browser. A key takeaway of our work is that we need to fundamentally rethink optimizations for mobile browsers.