Measuring quality ofWeb users experience (WebQoE) faces the following trade-off. On the one hand, current practice is to resort to metrics, such as the document completion time (onLoad), that are simple to measure though knowingly inaccurate. On the other hand, there are metrics, like Google's SpeedIndex, that are better correlated with the actual user experience, but are quite complex to evaluate and, as such, relegated to lab experiments. In this paper, we first provide a comprehensive state of the art on the metrics and tools available forWebQoE assessment. We then apply these metrics to a representative dataset (the Alexa top-100 webpages) to better illustrate their similarities, differences, advantages and limitations. We next introduce novel metrics, inspired by Google's SpeedIndex, that (i) offer significant advantage in terms of computational complexity, (ii) while maintaining a high correlation with the SpeedIndex at the same time. These properties makes our proposed metrics highly relevant and of practical use.
Measuring the quality of experience of web users / Bocchi, Enrico; DE CICCO, Luca; Rossi, Dario. - (2016), pp. 37-42. (Intervento presentato al convegno 2016 ACM SIGCOMM Workshop on QoE-Based Analysis and Management of Data Communication Networks, Internet-QoE 2016 tenutosi a Florianopolis, Brasil nel August 22-26, 2016) [10.1145/2940136.2940138].
Measuring the quality of experience of web users
DE CICCO, Luca;
2016-01-01
Abstract
Measuring quality ofWeb users experience (WebQoE) faces the following trade-off. On the one hand, current practice is to resort to metrics, such as the document completion time (onLoad), that are simple to measure though knowingly inaccurate. On the other hand, there are metrics, like Google's SpeedIndex, that are better correlated with the actual user experience, but are quite complex to evaluate and, as such, relegated to lab experiments. In this paper, we first provide a comprehensive state of the art on the metrics and tools available forWebQoE assessment. We then apply these metrics to a representative dataset (the Alexa top-100 webpages) to better illustrate their similarities, differences, advantages and limitations. We next introduce novel metrics, inspired by Google's SpeedIndex, that (i) offer significant advantage in terms of computational complexity, (ii) while maintaining a high correlation with the SpeedIndex at the same time. These properties makes our proposed metrics highly relevant and of practical use.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
