QoE Estimation for Web Service Selection Using a Fuzzy-Rough Hybrid Expert System
13 May 2014-pp 629-634
TL;DR: A novel method based on a fuzzy-rough hybrid expert system for estimating QoE of web services for web service selection is proposed and the simulation results show that the estimated web quality from the system has a high correlation with the subjectiveQoE obtained from the participants in controlled tests.
Abstract: With the proliferation of web services on the Inter-net, it has become important for service providers to select the best services for their clients in accordance to their functional and non-functional requirements. Generally, QoS parameters are used to select the most performing web services, however, these parameters do not necessarily reflect the user's satisfaction. Therefore, it is necessary to estimate the quality of web services on the basis of user satisfaction, i.e., Quality of Experience(QoE). In this paper, we propose a novel method based on a fuzzy-rough hybrid expert system for estimating QoE of web services for web service selection. It also presents how different QoS parameters impact the QoE of web services. For this, we conducted subjective tests in controlled environment with real users to correlate QoS parameters to subjective QoE. Based on this subjective test, we derive membership functions and inference rules for the fuzzy system. Membership functions are derived using a probabilistic approach and inference rules are generated using Rough Set Theory (RST). We evaluated our system in a simulated environment in MATLAB. The simulation results show that the estimated web quality from our system has a high correlation with the subjective QoE obtained from the participants in controlled tests.
Citations
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TL;DR: This paper provides an overview of the field of QoE assessment approaches including three aspects: subjective assessment, objective assessment, and hybrid assessment, which includes the definition, influence factors, and quantification approaches for QOE.
Abstract: With the development of converged all-IP networks, new network services—supporting, among others, image, audio, and video—with high diversity emerge, which brings huge pressure and fierce competition to service providers because of increasing choices for users Assessing end user’s quality of experience (QoE) has become one of the main issues to meet high user requirements and satisfaction under any circumstances, which means receiving any service, anytime, anywhere, on any device, by any media and networking technology, across any operator domain In this paper, we provide an overview of the field of QoE assessment approaches including three aspects: subjective assessment, objective assessment, and hybrid assessment The discussion includes the definition, influence factors, and quantification approaches for QoE Then, these three assessment methodologies are surveyed and compared across a number of factors This paper also introduces challenges and opportunities in the area of QoE assessment
38 citations
Cites methods from "QoE Estimation for Web Service Sele..."
...The model correlates QoS parameters directly withQoE perceptions by adopting appropriate datamodels such as discriminant analysis approach based on statistics [19], [31], regression analysis [74]–[76], support vector machine [77]–[79], decision tree [20], [78], [80] and rough set [81]....
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TL;DR: The latest developments in QoE in multimedia services, network management, VoIP, web development, games and cloud computing are presented.
Abstract: Over the last decade, there has been an exponential growth of services over the Internet. Services such as online gaming are gaining popularity, whereas, Voice over Internet (VoIP) has been well established. Storage, management and processing of data have moved away from the devices to the cloud. Quality of experience (QoE) defined as the user’s perception of service quality is utilized in different technologies, services and products for improving end user satisfaction and provides quality of service (QoS) according to their demands. In this paper, we describe the utilization of QoE in different technologies, services and products for improving end user satisfaction. Specifically, we present the latest developments in QoE in multimedia services, network management, VoIP, web development, games and cloud computing. Finally, we discuss the current research, solutions and present open issues of future research in QoE.
37 citations
Cites background from "QoE Estimation for Web Service Sele..."
...The web traffic suffers from network issues from host to end user which results in 90% of users to join other service providers due to low QoS (Pokhrel et al. 2014)....
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01 Sep 2014
TL;DR: This work presents a methodology and a system based on Random Neural Network (RNN) to analyze the impact of different MAC-level parameters on video QoE over IEEE 802.11n wireless networks and shows that the proposed method can effectively measure the impact.
Abstract: Supporting real time video transmission over wireless networks is very challenging. The video flow characteristics can be vulnerable to the time-varying channel conditions and the unreliability of the MAC (Medium Access Control) can substantially degrade Quality of Experience (QoE) in wireless networks. QoE reflects the satisfaction level of a user towards a particular service or application. It can be a valuable input to service providers and help them to enhance the quality of the overall system. In this work, we present a methodology and a system based on Random Neural Network (RNN) to analyze the impact of different MAC-level parameters on video QoE over IEEE 802.11n wireless networks. At first, subjective tests are performed to correlate MAC-level parameters with user's perceived video QoE. Secondly, we propose a RNN technique to estimate the impact of these parameters on video QoE. The experimental results show that the proposed method can effectively measure the impact of MAC-level parameters on video QoE. The acceptable correlation between subjective QoE and estimated QoE verifies the validity of the obtained results.
26 citations
Cites methods from "QoE Estimation for Web Service Sele..."
...Objective methods reduce the cost and time of estimation, however, it increases error margin....
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01 Jun 2017
TL;DR: This work proposes to collect network parameters and modify video metrics thanks to user estimated MOS, network parameters measured such as RTT, Jitter, bandwidth and delay and objective parameters such as VQM, PSNR and SSIM and focuses on optimizing end-to-end QoE metrics.
Abstract: In data services application over the internet, the user perception and satisfaction can be assessed by Quality of Experience (QoE) metrics. As QoE depends on both the users' perception and the used service, they form end-to-end metrics. While network optimization has traditionally focused on optimizing network properties such as QoS, in this work, we focus on optimizing end-to-end QoE metrics and hope to deliver to the client a good QoE and monitor it on real time. We argue that end-user QoE is the measure that is relevant for network operators and service providers. In today's world, video streaming rose above all other types of traffic. In fact, providing this service with a high quality presents the most challenging task among the advancements in networking technologies. Researchers are trying to help creating a more efficient network where congestion, broadband limitations and skyrocketing number of users present ever-diminishing obstacles. When it comes to us, we present in this paper a machine learning approach combined with adaptive video delivery service in order to provide a better QoE for video streaming services. This solution will be established using an SDN architecture. We can justify this choice because we need a centralized architecture, where the totality of the network is known, to predict its status. First part of the paper deals with a brief introduction of QoE and mathematical tools helping to model it. A synthetic study is done for this purpose. Second part describes the SDN networks, to see QoE requirement and service architecture to make simple the simulation deployment phase. The third part of the paper expose our proposed architecture, it describes the hole modules still the Rating Web application, ML model for predicting MOS, adaptive QoE monitoring concept, until the architecture of simulated environment. This application proposes to collect network parameters and modify video metrics thanks to user estimated MOS, network parameters measured such as RTT, Jitter, bandwidth and delay and objective parameters such as VQM, PSNR and SSIM. We highlight at the end the future of our proposition.
23 citations
TL;DR: Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods and could be useful for practicing managers developing expert systems under uncertainty.
Abstract: Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.,The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.,The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.,Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.
20 citations
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"QoE Estimation for Web Service Sele..." refers background in this paper
...A high amount of research attention has been diverted towards estimating QoE and correlating network QoS with QoE of multimedia services as shown in [8], [13] and [14]....
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Virtually all the literature on artificial intelligence is expressed in the jargon of commuter science, crowded with complex matrix algebra and differential equations. Unlike many other books on computer intelligence, this one demonstrates that most ideas behind intelligent systems are simple and straightforward. The book has evolved from lectures given to students with little knowledge of calculus, and the reader needs no prerequisites associated with knowledge of any programming language. The methods used in the book have been extensively tested through several courses given by the author.
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TL;DR: This tutorial explores the most salient and stable specifications in each of the three major areas of the emerging Web services framework, which are the simple object access protocol, the Web Services Description Language and the Universal Description, Discovery, and Integration directory.
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1,470 citations
"QoE Estimation for Web Service Sele..." refers background in this paper
...I. INTRODUCTION Web Services (WSs) are self-contained software systems that can be published, advertised, located and invoked through the web, usually relying in standardized XML technologies (REST, SOAP, WSDL, and UDDI [1]) for description and publication, and on Internet Protocols for…...
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