AI

EYEORG: A Platform For Crowdsourcing Web Quality Of Experience Measurements

Abstract

Tremendous effort has gone into the ongoing battle to make webpages load faster. This effort has culminated in new protocols (QUIC, SPDY, and HTTP/2) as well as novel content delivery mechanisms. In addition, companies like Google and SpeedCurve investigated how to measure “page load time” (PLT) in a way that captures human perception. This is challenging since even estimating when a web page is loaded has proven to be difficult in modern web pages. In this paper we present Eyeorg [13], a platform for crowdsourcing web quality of experience measurements. Eyeorg overcomes the scaling and automation challenges of recruiting users and collecting consistent user-perceived quality measurements. We validate Eyeorg’s capabilities via a set of 100 trusted participants. Next, we showcase its functionalities via three measurement campaigns, each involving 1,000 paid participants, to 1) study the quality of several PLT metrics, 2) compare HTTP/1.1 and HTTP/2 performance, and 3) assess the impact of online advertisements and ad blockers on user experience. We find that commonly used, and even novel and sophisticated PLT metrics fail to represent actual human perception of PLT, that the performance gains from HTTP/2 are imperceivable in some circumstances, and that not all ad blockers are created equal.