A new proposal to provide estimation of QoS and QoE over WiMAX networks: An approach based on computational intelligence and discrete-event simulation

Machado, Victor A.; Silva, Carlos N.; Oliveira, R. S.; Pessoa, Alexandre; Silva, Marcelino; Francês, Carlos Renato Lisboa; Costa, João Crisóstomo Weyl Albuquerque; Vijaykumar, Nandamudi Lankalapalli; Hirata, Celso Massaki

doi:10.1109/latincom.2011.6107419

Cited by 21 publications

(13 citation statements)

References 9 publications

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“…Academic research works done in Qian et al are based on SVM to deal with QoE prediction. Some relevant research works done in Paudel et al, Menkovski et al, and Rodr'ıguez et al to make QoE estimation are based on SVM, discriminate analysis, decision tree, neural network, Bayesian, and random neural networks (RNN), while Machado et al based their study on artificial neural network (ANN) to estimate QoE metrics in WiMAX networks or on KNN in Kang et al…”

Section: Related Work On Big Data Tools and Machine Learning Algorithmsmentioning

confidence: 99%

“…At first, we distinguish the relationship between QoS and QoE like it was studied in Fiedler et al and Khan et al 23,24 Several research works discussed different approaches based on ML for predicting QoE. 25 So, Le Callet et al 26 [33][34][35] to make QoE estimation are based on SVM, discriminate analysis, [36][37][38][39] decision tree, neural network, 36,40,[43][44][45][46] Bayesian, and random neural networks (RNN), while Machado et al 41 based their study on artificial neural network (ANN) to estimate QoE metrics in WiMAX networks or on KNN in Kang et al 42 ML algorithms help to deduce knowledge from stocked data and enable automation in the area of QoS and QoE management for operators and services providers. Different learning paradigms and ML techniques are applied to solve prediction problem in the QoE context by predicting MOS or estimating the user expectation before serving them (video or web pages).…”

Section: • K-nearest Neighbormentioning

confidence: 99%

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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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Section: Related Work On Big Data Tools and Machine Learning Algorithmsmentioning

confidence: 99%

Section: • K-nearest Neighbormentioning

confidence: 99%

WBQoEMS: Web browsing QoE monitoring system based on prediction algorithms

Letaïfa

2019

Int J Communication

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“…Furthermore, depending on the QoE values type, offline batch models can be divided into two groups. The first group uses the regression analysis to approximate the QoE as a continuous function of QoS parameters like in [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].The second group uses classification methods to predict the QoE class [25][26][27][28][29]. In the following sections, we briefly describe these models.…”

Section: Qoe-qos Correlation Models Using MLmentioning

confidence: 99%

“…We differentiate between those using Least Squares Regression (LSR) [8][9][10][11][12][13][14][15][16][17][18][19][20] and those using Regression Neural Networks (RNN) [21][22][23][24].…”

Section: A Models Using Regression Analysismentioning

confidence: 99%

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Survey on machine learning-based QoE-QoS correlation models

2014

2014 International Conference on Computing, Management and Telecommunications (ComManTel)

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The machine learning provides a theoretical and methodological framework to quantify the relationship between user OoE (Quality of Experience) and network QoS (Quality of Service). This paper presents an overview of QoE-QoS correlation models based on machine learning techniques. According to the learning type, we propose a categorization of correlation models. For each category, we review the main existing works by citing deployed learning methods and model parameters (QoE measurement, QoS parameters and service type). Moreover, the survey will provide researchers with the latest trends and findings in this field.

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