WBQoEMS: Web browsing QoE monitoring system based on prediction algorithms

Abstract

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.

Threshold‐oblivious on‐line web QoE assessment using neural network‐based regression model

“…However, it is hard to apply clustering to the classification of time series due to the large dimensions of the latter. Ben-Letaifa [14] analysed the impact of network behaviour on web service quality and proposed the QoE tool based on prediction algorithms and deep learning. The data set they used encompassed personal attributes (age, genre, and look), web page attributes (site type, content, and Index of site content), and network conditions (downstream speed, bandwidth, and delay).…”

“…To obtain a more accurate user experience, the concept of quality of experience (QoE) is concerned as an alternative to QoS by the ICT industry and internet service providers (ISPs) in recent years. ISP and device vendors can have an estimation of the current user experience anywhere in the network by using device traffic to create a hypothetical QoE prediction model [9][10][11][12][13][14][15].…”

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

“…In [41], the authors used a real-world QWS Dataset [2] for QoS attribute values and the Amazon customer review feedback dataset [3] for QoE values. However, QoE remains subjective, and its evaluation is expensive and tedious, requiring a high human involvement [6]. So, considering that users cannot invoke all WS to obtain QoE due to increased time cost and huge resource overhead [49], [53], the involvement and use of domain experts to obtain WS QoSE values makes sense and is applicable in this paper.…”