The impact of network performance on user experience is important to know, as it determines the success or failure of a service. Unfortunately, it is very difficult to assess it in real-time on an operational network. Monitoring of network-level performance criteria is easier and more usual. But the problem is then to correlate these network-level Quality of Service (QoS) to the Quality of Experience (QoE) perceived by the users. Efforts have been done in the previous years to map user behaviour to traffic characteristics on the network to QoS. However, being able to successfully relate these traffic characteristics to user satisfaction is not a simple task and still requires further investigations. In this work, we try to associate on one side the correlations between various traffic characteristics measured on an operational network and on the other side the user experience tested on an experimental platform. Our aim is to observe some pronounced trends regarding relationships between both types of results. More precisely, we want to validate how and to what extent the volumes of user sessions represent the level of user satisfaction. Along this way, we need to revise classical relationships between some of the network performance indicators such as loss, download time and throughput in order to strengthen the understanding of this impact on each other and on user satisfaction. This preliminary study is based on the application web.
Mobile connectivity typically exhibits on-off behavior, i.e. phases of undisturbed data transmission are interrupted by outages. Recent matching efforts have shown that the durations of the on-and off-phases can be matched by exponential distributions. The resulting exponential on-off models allow for elegant close-form solutions for performance metrics such as freeze probabilities in face of buffering, which amongst others allows for analysis-based interpretations of the impacts of various key parameters. Centering around exponential on-off behavior of mobile channels, this work provides a bridge between traffic measurements in mobile environments, closed-form traffic analysis based on Markov-modulated fluid flow models, and user perception of those kinds of disturbances that are typical for mobile environments. It also shows the need to focus Quality of Experience (QoE) studies on impact factors close to the source of the performance degradation, rather than on generic Quality of Service (QoS) parameters on packet level.
Abstract-Brief episodes of network faults and performance issues adversely affect the user Quality of Experience (QoE). Besides damaging the current opinions of users, these events may also shape user's future perception of the service. Therefore, it is important to quantify the impact of such events on QoE over time. In this paper, we present our findings on the temporal aspects of user feedback to disturbances on networks. These findings are based on subjective user tests performed in the context of web browsing on an e-commerce website. The results of this study suggest that the QoE drops significantly every time the page load time grows. The after-effects of network disturbances on user QoE remain visible even when the network problems are over, i.e., users do not immediately return to the same level of opinion scores as compared to the corresponding pre-disturbance phase. They tend to remember their recent experiences. Our results also show that there are four segments of users that exist with regards to their feedback to page load times. Network operators may customize their services according to each segment of users to raise the overall QoE. Finally, we show that the exponential relationship provides best fits of QoE and page load times for all segments of users.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.