Modelling of Quality of Experience for Web Traffic

Nguyen, Le‐Thu T.; Harris, Richard; Jusak, Jusak; Punchihewa, Amal

doi:10.1109/netapps.2010.22

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…As a result of work in [14] [15], it is observed that the users' scores in the experiment follow our intuition or obey a "rule of nature". For example, the users' scores tend to be "Dissatisfied" when we increase the delay.…”

Section: Rule Of Naturementioning

confidence: 66%

“…From the evaluation mentioned in [16] and [15], it is concluded that there is general agreement between the users' perception and increased values for both objective metrics of network and application performance and we called it "the rule of nature" as shown in Figure 4. However to understand further changes in specific cases of users' perception and for un-matching cases of our "rule of nature" as described by the red line rectangles in Figure 5, we proposed a further metric that is related to content into our customer QoE for Web browsing model.…”

Section: B Aim Of Content Testingmentioning

confidence: 90%

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Assessment of quality of experience for web browsing-as function of quality of service and content factors

Nguyen

Harris

Punchihewa

2013

2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN)

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Evaluation of QoE for web browsing is a challenging topic as it involves many different factors related to human as well as technical issues. To understand such a large number of human and technical metrics is both difficult and tricky. Two main objectives of this paper are to propose and develop a new QoE assessment approach for web browsing. The proposed QoE assessment of Web based services uses a comprehensive set of metrics in order to construct a full and detailed understanding of users' experiences in a web based service environment. The analysis and validation of QoE assessment of Web based traffic using both objective parameters from networking, application, and content perspectives have shown the efficacy of the proposed assessment. A comprehensive set of metrics has been evaluated on the overall web user QoE.

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Section: Rule Of Naturementioning

confidence: 66%

Section: B Aim Of Content Testingmentioning

confidence: 90%

Assessment of quality of experience for web browsing-as function of quality of service and content factors

Nguyen

Harris

Punchihewa

2013

2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN)

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“…[12] (which, in addition to session lengths, uses a large range of other metrics to directly infer QoE). Session lengths can be estimated as the lengths of periods during which requests and responses occur with certain regularity and which are separated by periods of no such activities.…”

Section: Http Requestmentioning

confidence: 99%

“…However, both methods, as well as applications of the latter, e.g. [10], [11], and other methods such as [12], all consider single TCP connections although it is well known that web pages typically consist of multiple TCP flows. We thus take this one step further and group TCP flows to web pages after which network performance is measured on and user behaviour is linked to such "reassembled" pages.…”

Section: Introductionmentioning

confidence: 99%

Detecting user dissatisfaction from passive monitoring

2013

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In an increasingly competitive environment it is more important than ever for operators to keep their end users satisfied. User satisfaction is often characterised in terms of Quality of Experience (QoE), a subjective metric with multiple dimensions such as expectations, content, terminal, environment, cost and performance. QoE is typically quantified as MOS, mean opinion score, which is obtained by averaging the ranks of a number of voluntary users for controlled combinations content/terminals/performance etc. While this approach has many advantages, there are also a number of difficulties such as representativeness (the number of users as well as the number of objects and devices all have to be kept small); validity (the results may be biased by the situation, the setting, the renumeration and so on); and applicability (it is not clear how different numbers map to notions such as "acceptable" or "unacceptable" and operators alone cannot do very much about factors such as content).We thus investigate the possibilities of detecting user opinions in the above, simplified, terms and from the network itself; with actual expectations, content, terminals, environments, costs and performance for virtually all users all the time. To this end we revisit the earlier suggestion that user opinions be reflected in their behaviour such that poor performance may result in interrupted requests. These works have, however, considered single flows hence we extend that idea to web pages which are groups of flows. In this paper we present our methods to group flows, interpret users, and characterise performance and we make a first assessment of the correlations between web page interruptions and network performance characteristics.

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“…The study in [6] has contributed to user-centred design where a QoS-QoE correlation model for objective QoE was proposed. Recent articles [7] [8], suggest a comprehensive look at the influencing factors for QoE as shown in Figure 1. These factors should be not only traditional QoS but also application specific features, and environmental, psychological, and sociological aspects including user expectations and experience with similar services, other opinions, pricing policies, feature of particular location where service is received, etc.…”

Section: Introductionmentioning

confidence: 99%

Analysis of networking and Application Layer derived metrics for web Quality of Experience

Nguyen

Harris

Jusak

2012

2012 IEEE Consumer Communications and Networking Conference (CCNC)

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Subjective assessment for measuring the Quality of Experience may require time-consuming and often expensive methods, and yet, quantitative and accurate user scores are desired. In order to obtain valid correlation between analytical model and user scores, assessment based on networking perspectives and human perception is required. In this study, we have utilized the orthogonal arrays using the Taguchi approach to construct the experiment to characterize the application as well as network performance metrics in our QoE assessment model for web-based systems.

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Modelling of Quality of Experience for Web Traffic

Cited by 6 publications

References 6 publications

Assessment of quality of experience for web browsing-as function of quality of service and content factors

Assessment of quality of experience for web browsing-as function of quality of service and content factors

Detecting user dissatisfaction from passive monitoring

Analysis of networking and Application Layer derived metrics for web Quality of Experience

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