Content Matters: Clustering Web Pages for QoE Analysis With WebCLUST

Abstract

The properties of a web page have a strong impact on its overall loading process, including the download of its contents and their progressive rendering at the browser. As a consequence, web page content has a strong impact on the experience of web users. In this paper, we present WebCLUST, a clustering-based classification approach for web pages, which groups pages into quality-meaningful content classes impacting the Quality of Experience (QoE) of the users. Groups are defined based on standard Multipurpose Internet Mail Extensions (MIME) content breakdown and external subdomain connections, obtained through in-browser, application level measurements. Using a large corpus of multi-device, heterogeneous web content and QoE-relevant measurements for the top-500 most popular websites in the Internet, we show how WebCLUST can automatically identify relevant web content classes showing significantly different performance in terms of Web QoE relevant metrics, such as Speed Index. We additionally evaluate the impact of content caching and device type on the identification performance of WebCLUST, showing how content classes might look significantly different, depending on the access device type (desktop vs mobile), as well as when considering browser caching. Our findings suggest that Web QoE assessment should explicitly consider page content and subdomain embedding within the analysis, especially when it comes to recent work on Web QoE inference through machine learning models. To the best of our knowledge, this is the first study showing the impact of web content on Web QoE metrics, opening the door to new Web QoE assessment strategies.

Not all Web Pages are Born the Same Content Tailored Learning for Web QoE Inference

“…The dataset includes both applicationlayer Web QoE metrics -such as SI, as well as network traffic traces, for 15,000 web page loading sessions (i.e., the loading of a single browser web page). Finally, while we have recently worked on the problem of clustering web pages based on content characteristics [3], this paper goes beyond our previous work and the state of the art, by enhancing Web QoE inference through content-tailored machine learning. In particular, we show that our combined, content-tailored approach improves the inference performance of the SI by almost 30% with respect to previous one-fit-all single model approach, additionally reducing the QoE inference error in terms of Mean Opinion Scores (MOS) by more than 40%.…”