Featured Publications

Performance Evaluation, vol. 165, 2024. Elsevier.

While users of Internet multimedia services demand high Quality of Experience (QoE), meeting these demands results in energy consumption along the service delivery path, from the end user’s device, through the network to the service infrastructure (e.g., in a cloud). This energy consumption typically implies carbon dioxide (CO2) emissions, which are a primary driver of climate change. One contribution of this article is to quantify and illustrate the trade-off between the QoE of video streaming services and CO2 emissions. Kleinrock’s power metric from queuing theory is applied to find operational points and recommended video bitrates of the QoE-sustainability tradeoff. Furthermore, considering that networks and service delivery infrastructures are still in the process of transitioning towards ‘green energy’ consumption, we investigate the impact of ‘green users’ accepting certain quality degradations so as to reduce CO2 emissions. Our discussions focus on two aspects: Is it more relevant to focus on green user behavior or green networking technology today and in the future in year 2030? What are the implications of solution approaches on the networking and communications technology?
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IEEE Communications Magazine, doi: 10.1109/MCOM.006.2200490, pp:1-7

While users of Internet multimedia services demand high Quality of Experience (QoE), meeting these demands results in energy consumption along the service delivery path, from the end user’s device, through the network to the service infrastructure (e.g., in a cloud). This energy consumption typically implies carbon dioxide (CO2) emissions, which are a primary driver of climate change. One contribution of this article is to quantify and illustrate the trade-off between the QoE of video streaming services and CO2 emissions. Kleinrock’s power metric from queuing theory is applied to find operational points and recommended video bitrates of the QoE-sustainability tradeoff. Furthermore, considering that networks and service delivery infrastructures are still in the process of transitioning towards ‘green energy’ consumption, we investigate the impact of ‘green users’ accepting certain quality degradations so as to reduce CO2 emissions. Our discussions focus on two aspects: Is it more relevant to focus on green user behavior or green networking technology today and in the future in year 2030? What are the implications of solution approaches on the networking and communications technology?
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IEEE Communications Letters, vol. 21, no. 1, pp. 184-187

User-centric service and application management focuses on the Quality of Experience (QoE) as perceived by the end user. Thereby, the goal is to maximize QoE while ensuring fairness among users, e.g., for resource allocation and scheduling in shared systems. Although the literature suggests to consider consequently QoE fairness, there is currently no accepted definition of QoE fairness. The contribution of this paper is the definition of a generic QoE fairness index $F$ which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain’s fairness index. Based on results, we argue that neither QoS fairness nor Jain’s fairness index meet all of the desirable QoE-relevant properties which are met by $F$. Consequently, the proposed index $F$ may be used to compare QoE fairness across systems and applications, thus serving as a benchmark for QoE management mechanisms and system optimization.
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Quality and User Experience 1(1):1-23, Springer.

While Quality of Experience (QoE) has advanced very significantly as a field in recent years, the methods used for analyzing it have not always kept pace. When QoE is studied, measured or estimated, practically all the literature deals with the so-called Mean Opinion Score (MOS). The MOS provides a simple scalar value for QoE, but it has several limitations, some of which are made clear in its name: for many applications, just having a mean value is not sufficient. For service and content providers in particular, it is more interesting to have an idea of how the scores are distributed, so as to ensure that a certain portion of the user population is experiencing satisfactory levels of quality, thus reducing churn. In this article we put forward the limitations of MOS, present other statistical tools that provide a much more comprehensive view of how quality is perceived by the users, and illustrate it all by analyzing the results of several subjective studies with these tools
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In proceedings of IEEE ICC QoE-FI, London, UK.

In contrast to the rather network-centric notion of Quality of Service (QoS), the concept of Quality of Experience (QoE) has a strongly user-centric perspective on service quality in communication networks as well as online services. However, related research on QoE so far has largely neglected the question of how to operationalize quality differentiation and to provide corresponding solutions tailored to the end users. In this paper, we argue that the introduction of Experience Level Agreements (ELA) as QoE-enabled counterpiece to traditional QoS-based Service Level Agreements (SLA) would provide a key step towards being able to sell service quality to the user. Hence, we investigate various ideas to exploit QoE awareness for improving SLAs (ranging from internal aspects like SLOs by service providers to completely novel definitions of ELAs which are able to characterize QoE explicitly), and discuss important problems and challenges of the proposed transition as well.
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In PIK - Praxis der Informationverarbeitung und -kommunikation, 37(4):265-274

In this paper we propose a methodological framework for modeling Quality of Experience (QoE) for media services in a generic manner. We consider QoE as a multi-dimensional concept dependent on several factors related to the service itself, its resource requirements, its users, and its context of use. As a first step, we group these factors into four factor spaces and propose a mapping of them into a QoE space. We then focus on the application of this mapping in the context of networked media services by adhering to a layered approach for modeling QoE dimensions in relation to the aforementioned QoE-affecting factors. Such an approach facilitates understanding a service’s QoE as a composite function of the performance of the underlying network, and the actual service implementation, under constraints imposed by some of the QoE-affecting factors. In order to illustrate the applicability of the proposed methodology, we present a case study for mobile video.
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In proceedings of MIPRO 2012, Opatija, Croatia.

Understanding and modeling the wide range of influence factors that impact end user Quality of Experience (QoE) and go beyond traditional Quality of Service (QoS) parameters has become an important issue for service and network providers, in particular for new and emerging services. In this paper we present a generic ARCU (Application-Resource-Context-User) Model which categorizes influence factors into four multi-dimensional spaces. The model further maps points from these spaces to a multi-dimensional QoE space, representing both qualitative and quantitative QoE metrics. We discuss examples of applying the ARCU Model in practice, and identify key challenges.
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IEEE Communications Magazine, 50(4):28-36

Cloud computing is currently gaining enormous momentum due to a number of promised benefits: ease of use in terms of deployment, administration and maintenance, high scalability and flexibility to create new services. However, as more personal and business applications migrate to the Cloud, the service quality will become an important differentiator between providers. In particular, Quality of Experience (QoE) as perceived by users has the potential become the guiding paradigm for managing quality in the Cloud. In this article, we discuss technical challenges emerging from shifting services to the Cloud, as well as how this shift impacts QoE and QoE management. Thereby, a particular focus is on multimedia Cloud applications. Together with a novel QoE-based classification scheme of cloud applications, these challenges drive the research agenda on QoE management for Cloud applications
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Computer Networks 52(6):1179-1192.

The conversational quality of a VoIP communication is dependent on several factors such as the coding process used, the network conditions and the type of error correction or concealment employed. Furthermore, the quality perceived by the users is also dependent on the characteristics of the conversation itself. Assessing this kind of communication is a very difficult problem, and most of the studies available in the literature simplify the issue by restricting the analysis to only one or two parameters. However, the number of potentially affecting factors is typically higher, and their joint effect on quality is complex. In this paper we study the combined effects of bit rate, forward error correction, loss rate, loss distribution, delay and jitter on the perceived conversational quality. In order to achieve this we use the Pseudo-Subjective Quality Assessment (PSQA) technique, which allows us to obtain accurate, subjective–like assessments, in real time if necessary. Our contributions are thus two–fold: firstly, we offer a detailed analysis of the impact of these parameters and their interactions on the perceived conversational quality. Secondly, we show how the PSQA methodology can be used to provide accurate conversatinal quality estimations.
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Performance Evaluation, 57(2):141-162

This paper addresses the problem of quantitatively evaluating the quality of a speech stream transported over the Internet as perceived by the end-user. We propose an approach being able to perform this task automatically and, if necessary, in real time. Our method is based on using G-networks (open networks of queues with positive and negative customers) as Neural Networks (in this case, they are called Random Neural Networks) to learn, in some sense, how humans react vis-à-vis a speech signal that has been distorted by encoding and transmission impairments. This can be used for control purposes, for pricing applications, etc. Our method allows us to study the impact of several source and network parameters on the quality, which appears to be new (previous work analyzes the effect of one or two selected parameters only). In this paper, we use our technique to study the impact on performance of several basic source and network parameters on a non-interactive speech flow, namely loss rate, loss distribution, codec, forward error correction, and packetization interval, all at the same time. This is important because speech/audio quality is affected by several parameters whose combined effect is neither well identified nor understood
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While users of Internet multimedia services demand high Quality of Experience (QoE), meeting these demands results in energy …
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While users of Internet multimedia services demand high Quality of Experience (QoE), meeting these demands results in energy …
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Software Log anomaly event detection with masked event prediction has various technical approaches with countless configurations and …
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It is a natural thing that users of multimedia services want to have the highest possible Quality of Experience (QoE), when using said …
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As stability testing execution logs can be very long, software engineers need help in locating anomalous events to investigate when …
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In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system …
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With Quality of Experience (QoE) research having made significant advances over the years, increased attention is being put on …
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