Measuring the Quality of Experience of Web users

Bocchi, Enrico; Cicco, Luca De; Rossi, Dario

doi:10.1145/2940136.2940138

Cited by 37 publications

(30 citation statements)

References 4 publications

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“…Instead, this class integrates the loading time over all events of a given type throughout the evolution of a page progress. Following Google's original SpeedIndex (SI) [3] definition, a number of generalizations have been proposed in the literature [8,15]. All the metrics in this class fit the general form:…”

Section: Web Qos Metricsmentioning

confidence: 99%

“…ObjectIndex (OI) and ByteIndex (BI) [8] use the percentage of objects (and bytes) downloaded until the PLT. Finally, PerceptualSpeedIndex (PSI) [15] uses Structural Similarity to measure the visual progress x(t) and cut the time horizon at either the PLT, or at an arbitrary time earlier than PLT.…”

Section: Web Qos Metricsmentioning

confidence: 99%

“…The metric most commonly used to measure the performance of Web browsing has been the Page Load Time (PLT), which holds true for both research [13,26,21,27,25] and industry [1,2,4]. Recent studies [10,3,8,18,24,15], however, started to question the relevance of using PLT to measure quality of user experience. The main skepticism is that whereas PLT measures the precise time at which the page finishes loading, the experience of the user depends on the whole process up to that time and the rendering time at the browser.…”

Section: Introductionmentioning

confidence: 99%

“…The main skepticism is that whereas PLT measures the precise time at which the page finishes loading, the experience of the user depends on the whole process up to that time and the rendering time at the browser. As such, a number of alternative metrics, which we review in Section 2.1, such as the Above-the-Fold (ATF) time [10], SpeedIndex [3], Object/ByteIndex [8] and PerceptualSpeedIndex [15] have been proposed to bridge this gap.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Narrowing the Gap Between QoS Metrics and Web QoE Using Above-the-fold Metrics

Hora

Asrese

Christophides

et al. 2018

Lecture Notes in Computer Science

Self Cite

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Abstract. Page load time (PLT) is still the most common application Quality of Service (QoS) metric to estimate the Quality of Experience (QoE) of Web users. Yet, recent literature abounds with proposals for alternative metrics (e.g., Above The Fold, SpeedIndex and variants) that aim at better estimating user QoE. The main purpose of this work is thus to thoroughly investigate a mapping between established and recently proposed objective metrics and user QoE. We obtain ground truth QoE via user experiments where we collect and analyze 3,400 Web accesses annotated with QoS metrics and explicit user ratings in a scale of 1 to 5, which we make available to the community. In particular, we contrast domain expert models (such as ITU-T and IQX) fed with a single QoS metric, to models trained using our ground-truth dataset over multiple QoS metrics as features. Results of our experiments show that, albeit very simple, expert models have a comparable accuracy to machine learning approaches. Furthermore, the model accuracy improves considerably when building per-page QoE models, which may raise scalability concerns as we discuss.

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Section: Web Qos Metricsmentioning

confidence: 99%

Section: Web Qos Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Narrowing the Gap Between QoS Metrics and Web QoE Using Above-the-fold Metrics

Hora

Asrese

Christophides

et al. 2018

Lecture Notes in Computer Science

Self Cite

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“…In this context, Balachandran et al 8 provides a comprehensive state of the art on the WebQoE parameters and metrics. Bocchi et al 9 divide the web QoE metrics in specially three categories: First category (a) Time-instant metrics, which includes TTF: time at which the first byte of payload is received, DOM: time at which the document object model (DOM) is loaded, TTFP: time at which the first object is painted, onLoad: time at which all bytes of payload have been received, ATF: time at which the content "above the fold" has been rendered; second category (b) Time-integral metrics: ByteIndex and the ObjectIndex, which generalize the SpeedIndex proposed by Google. Then, the third category (c) Compound scores which includes YSlow, 10 Yahoo's compound score (23 weighted heuristics) and PageSpeed, 7 Google's PageSpeed Insight heuristics dynaTrace.…”

Section: Context and Problem Positionmentioning

confidence: 99%

WBQoEMS: Web browsing QoE monitoring system based on prediction algorithms

Letaïfa

2019

Int J Communication

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Summary These years, researchers are interested in the web users perceived Quality of Experience called web QoE. Monitoring web browsing QoE can help service providers to determine if network conditions are contributing to user satisfaction. Making a relationship between web QoE and network parameters helps them to evaluate potential solutions in order to increase user satisfaction. Recent literature use metrics such as page load, wait time, or SpeedIndex to estimate the QoE of web users. However, QoE is a measure that remains subjective, and its evaluation is expensive and tedious because it requires a high human participation. Using Big Data Analytics, web service providers can anticipate user's behavior and take necessary actions which lead to better QoS/QoE. In this work, we have shown that we can make a relationship between QoS and QoE and predict QoE thanks to machine learning (ML) algorithms or deep learning approach. In fact, these new prediction solutions can help in prediction of the web QoE. Yet, this work focuses on some key performance indicators in the field of web browsing where Big Data Analytics are used. It gives a predictive solution based on Big Data tools to monitor and work toward enhancing QoS and QoE for the web users. Therefore, the design of a tool that can objectively measure this QoE with reasonable accuracy and in real time has become a primary need which is an interesting challenge to solve. As a first contribution, we analyzed the impact of network behavior on the quality of web services, then described some relevant research works done in ML field to predict QoE. We finally give the proposed tool based on prediction algorithms from ML and deep learning both used to monitor web QoE.

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Measuring Web Quality of Experience in Cellular Networks

Asrese

Walelgne

Bajpai

et al. 2019

Lecture Notes in Computer Science

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I am immensely thankful to my families and relatives for their unconditional love and support through my school life. Last but not least, I would like to thank my better half, Ichalem, for her love and everything else. This thesis is dedicated to my mother Tena Yihunie -who is my role model of perseverance and confidence -and Adisualem -youngest brother, who was born three weeks after this research was started.

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Measuring the Quality of Experience of Web users

Cited by 37 publications

References 4 publications

Narrowing the Gap Between QoS Metrics and Web QoE Using Above-the-fold Metrics

Narrowing the Gap Between QoS Metrics and Web QoE Using Above-the-fold Metrics

WBQoEMS: Web browsing QoE monitoring system based on prediction algorithms

Measuring Web Quality of Experience in Cellular Networks

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