@phdthesis{Loh2024,
  author    = {Loh, Frank},
  title     = {Monitoring the Quality of Streaming and Internet of Things Applications},
  edition   = {korrigierte Version},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-35096},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350969},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {The ongoing and evolving usage of networks presents two critical challenges for current and future networks that require attention: (1) the task of effectively managing the vast and continually increasing data traffic and (2) the need to address the substantial number of end devices resulting from the rapid adoption of the Internet of Things. Besides these challenges, there is a mandatory need for energy consumption reduction, a more efficient resource usage, and streamlined processes without losing service quality. We comprehensively address these efforts, tackling the monitoring and quality assessment of streaming applications, a leading contributor to the total Internet traffic, as well as conducting an exhaustive analysis of the network performance within a Long Range Wide Area Network (LoRaWAN), one of the rapidly emerging LPWAN solutions.},
  subject      = {Leistungsbewertung},
  language  = {en}
}
@phdthesis{Loh2024,
  author    = {Loh, Frank},
  title     = {Monitoring the Quality of Streaming and Internet of Things Applications},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-34783},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-347831},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {The ongoing and evolving usage of networks presents two critical challenges for current and future networks that require attention: (1) the task of effectively managing the vast and continually increasing data traffic and (2) the need to address the substantial number of end devices resulting from the rapid adoption of the Internet of Things. Besides these challenges, there is a mandatory need for energy consumption reduction, a more efficient resource usage, and streamlined processes without losing service quality. We comprehensively address these efforts, tackling the monitoring and quality assessment of streaming applications, a leading contributor to the total Internet traffic, as well as conducting an exhaustive analysis of the network performance within a Long Range Wide Area Network (LoRaWAN), one of the rapidly emerging LPWAN solutions.},
  subject      = {Leistungsbewertung},
  language  = {en}
}
@techreport{MetzgerSchroederRafetseder2021,
  type      = {Working Paper},
  author    = {Metzger, Florian and Schr{\"o}der, Svenja and Rafetseder, Albert},
  title     = {Subjective And Objective Assessment Of Video Game Context Factors},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242471},
  pages     = {7},
  year      = {2021},
  abstract  = {The recently published ITU-T Recommendation G1.032 proposes a list of factors that may influence cloud and online gaming Quality of Experience (QoE). This paper provides two practical evaluations of proposed system and context influence factors: First, it investigates through an online survey (n=488) the popularity of platforms, preferred ways of distribution, and motivational aspects including subjective valuations of characteristics offered by today's prevalent gaming platforms. Second, the paper evaluates a large dataset of objective metrics for various gaming platforms: game lists, playthrough lengths, prices, etc., and contrasts these metrics against the gamers' opinions. The combined data-driven approach presented in this paper complements in-person and lab studies usually employed.},
  subject      = {Videospiel},
  language  = {en}
}
@phdthesis{Moldovan2021,
  author    = {Moldovan, Christian},
  title     = {Performance Modeling of Mobile Video Streaming},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-22871},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228715},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {In the past two decades, there has been a trend to move from traditional television to Internet-based video services. With video streaming becoming one of the most popular applications in the Internet and the current state of the art in media consumption, quality expectations of consumers are increasing. Low quality videos are no longer considered acceptable in contrast to some years ago due to the increased sizes and resolution of devices. If the high expectations of the users are not met and a video is delivered in poor quality, they often abandon the service. Therefore, Internet Service Providers (ISPs) and video service providers are facing the challenge of providing seamless multimedia delivery in high quality. Currently, during peak hours, video streaming causes almost 58\\% of the downstream traffic on the Internet. With higher mobile bandwidth, mobile video streaming has also become commonplace. According to the 2019 Cisco Visual Networking Index, in 2022 79\% of mobile traffic will be video traffic and, according to Ericsson, by 2025 video is forecasted to make up 76\% of total Internet traffic. Ericsson further predicts that in 2024 over 1.4 billion devices will be subscribed to 5G, which will offer a downlink data rate of 100 Mbit/s in dense urban environments. One of the most important goals of ISPs and video service providers is for their users to have a high Quality of Experience (QoE). The QoE describes the degree of delight or annoyance a user experiences when using a service or application. In video streaming the QoE depends on how seamless a video is played and whether there are stalling events or quality degradations. These characteristics of a transmitted video are described as the application layer Quality of Service (QoS). In general, the QoS is defined as "the totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service" by the ITU. The network layer QoS describes the performance of the network and is decisive for the application layer QoS. In Internet video, typically a buffer is used to store downloaded video segments to compensate for network fluctuations. If the buffer runs empty, stalling occurs. If the available bandwidth decreases temporarily, the video can still be played out from the buffer without interruption. There are different policies and parameters that determine how large the buffer is, at what buffer level to start the video, and at what buffer level to resume playout after stalling. These have to be finely tuned to achieve the highest QoE for the user. If the bandwidth decreases for a longer time period, a limited buffer will deplete and stalling can not be avoided. An important research question is how to configure the buffer optimally for different users and situations. In this work, we tackle this question using analytic models and measurement studies. With HTTP Adaptive Streaming (HAS), the video players have the capability to adapt the video bit rate at the client side according to the available network capacity. This way the depletion of the video buffer and thus stalling can be avoided. In HAS, the quality in which the video is played and the number of quality switches also has an impact on the QoE. Thus, an important problem is the adaptation of video streaming so that these parameters are optimized. In a shared WiFi multiple video users share a single bottleneck link and compete for bandwidth. In such a scenario, it is important that resources are allocated to users in a way that all can have a similar QoE. In this work, we therefore investigate the possible fairness gain when moving from network fairness towards application-layer QoS fairness. In mobile scenarios, the energy and data consumption of the user device are limited resources and they must be managed besides the QoE. Therefore, it is also necessary, to investigate solutions, that conserve these resources in mobile devices. But how can resources be conserved without sacrificing application layer QoS? As an example for such a solution, this work presents a new probabilistic adaptation algorithm that uses abandonment statistics for ts decision making, aiming at minimizing the resource consumption while maintaining high QoS. With current protocol developments such as 5G, bandwidths are increasing, latencies are decreasing and networks are becoming more stable, leading to higher QoS. This allows for new real time data intensive applications such as cloud gaming, virtual reality and augmented reality applications to become feasible on mobile devices which pose completely new research questions. The high energy consumption of such applications still remains an issue as the energy capacity of devices is currently not increasing as quickly as the available data rates. In this work we compare the optimal performance of different strategies for adaptive 360-degree video streaming.},
  subject      = {Video{\"u}bertragung},
  language  = {en}
}
@phdthesis{Borchert2020,
  author    = {Borchert, Kathrin Johanna},
  title     = {Estimating Quality of Experience of Enterprise Applications - A Crowdsourcing-based Approach},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-21697},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216978},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Nowadays, employees have to work with applications, technical services, and systems every day for hours. Hence, performance degradation of such systems might be perceived negatively by the employees, increase frustration, and might also have a negative effect on their productivity. The assessment of the application's performance in order to provide a smooth operation of the application is part of the application management. Within this process it is not sufficient to assess the system performance solely on technical performance parameters, e.g., response or loading times. These values have to be set into relation to the perceived performance quality on the user's side - the quality of experience (QoE). This dissertation focuses on the monitoring and estimation of the QoE of enterprise applications. As building models to estimate the QoE requires quality ratings from the users as ground truth, one part of this work addresses methods to collect such ratings. Besides the evaluation of approaches to improve the quality of results of tasks and studies completed on crowdsourcing platforms, a general concept for monitoring and estimating QoE in enterprise environments is presented. Here, relevant design dimension of subjective studies are identified and their impact of the QoE is evaluated and discussed. By considering the findings, a methodology for collecting quality ratings from employees during their regular work is developed. The method is realized by implementing a tool to conduct short surveys and deployed in a cooperating company. As a foundation for learning QoE estimation models, this work investigates the relationship between user-provided ratings and technical performance parameters. This analysis is based on a data set collected in a user study in a cooperating company during a time span of 1.5 years. Finally, two QoE estimation models are introduced and their performance is evaluated.},
  subject      = {Quality of Experience},
  language  = {en}
}
@techreport{BlenkKellererHossfeld2020,
  type      = {Working Paper},
  author    = {Blenk, Andreas and Kellerer, Wolfgang and Hoßfeld, Tobias},
  title     = {Technical Report on DFG Project SDN-App: SDN-enabled Application-aware Network Control Architectures and their Performance Assessment},
  doi       = {10.25972/OPUS-20755},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207558},
  year      = {2020},
  abstract  = {The DFG project "SDN-enabled Application-aware Network Control Architectures and their Performance Assessment" (DFG SDN-App) focused in phase 1 (Jan 2017 - Dec 2019) on software defined networking (SDN). Being a fundamental paradigm shift, SDN enables a remote control of networking devices made by different vendors from a logically centralized controller. In principle, this enables a more dynamic and flexible management of network resources compared to the traditional legacy networks. Phase 1 focused on multimedia applications and their users' Quality of Experience (QoE). This documents reports the achievements of the first phase (Jan 2017 - Dec 2019), which is jointly carried out by the Technical University of Munich, Technical University of Berlin, and University of W{\"u}rzburg. The project started at the institutions in Munich and W{\"u}rzburg in January 2017 and lasted until December 2019. In Phase 1, the project targeted the development of fundamental control mechanisms for network-aware application control and application-aware network control in Software Defined Networks (SDN) so to enhance the user perceived quality (QoE). The idea is to leverage the QoE from multiple applications as control input parameter for application-and network control mechanisms. These mechanisms are implemented by an Application Control Plane (ACP) and a Network Control Plane (NCP). In order to obtain a global view of the current system state, applications and network parameters are monitored and communicated to the respective control plane interface. Network and application information and their demands are exchanged between the control planes so to derive appropriate control actions. To this end, a methodology is developed to assess the application performance and in particular the QoE. This requires an appropriate QoE modeling of the applications considered in the project as well as metrics like QoE fairness to be utilized within QoE management. In summary, the application-network interaction can improve the QoE for multi-application scenarios. This is ensured by utilizing information from the application layer, which are mapped by appropriate QoS-QoE models to QoE within a network control plane. On the other hand, network information is monitored and communicated to the application control plane. Network and application information and their demands are exchanged between the control planes so to derive appropriate control actions.},
  subject      = {Software-defined networking},
  language  = {en}
}
@techreport{Metzger2020,
  type      = {Working Paper},
  author    = {Metzger, Florian},
  title     = {Crowdsensed QoE for the community - a concept to make QoE assessment accessible},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203748},
  pages     = {7},
  year      = {2020},
  abstract  = {In recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays. However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own.},
  subject      = {Quality of Experience},
  language  = {en}
}
@phdthesis{DinhXuan2018,
  author    = {Dinh-Xuan, Lam},
  title     = {Quality of Experience Assessment of Cloud Applications and Performance Evaluation of VNF-Based QoE Monitoring},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-16918},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-169182},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {In this thesis various aspects of Quality of Experience (QoE) research are examined. The work is divided into three major blocks: QoE Assessment, QoE Monitoring, and VNF Performance Evaluation. First, prominent cloud applications such as Google Docs and a cloud-based photo album are explored. The QoE is characterized and the influence of packet loss and delay is studied. Afterwards, objective QoE monitoring for HTTP Adaptive Video Streaming (HAS) in the cloud is investigated. Additionally, by using a Virtual Network Function (VNF) for QoE monitoring in the cloud, the feasibility of an interworking of Network Function Virtualization (NFV) and cloud paradigm is evaluated. To this end, a VNF that exploits deep packet inspection technique was used to parse the video traffic. An algorithm is then designed accordingly to estimate video quality and QoE based on network and application layer parameters. To assess the accuracy of the estimation, the VNF is measured in different scenarios under different network QoS and the virtual environment of the cloud architecture. The insights show that the different geographical deployments of the VNF influence the accuracy of the video quality and QoE estimation. Various Service Function Chain (SFC) placement algorithms have been proposed and compared in the context of edge cloud networks. On the one hand, this research is aimed at cloud service providers by providing methods for evaluating QoE for cloud applications. On the other hand, network operators can learn the pitfalls and disadvantages of using the NFV paradigm for such a QoE monitoring mechanism.},
  subject      = {Quality of Experience},
  language  = {en}
}
@phdthesis{Seufert2017,
  author    = {Seufert, Michael Thomas},
  title     = {Quality of Experience and Access Network Traffic Management of HTTP Adaptive Video Streaming},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-15413},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-154131},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.},
  subject      = {Quality of Experience},
  language  = {en}
}
@phdthesis{Hirth2016,
  author    = {Hirth, Matthias Johannes Wilhem},
  title     = {Modeling Crowdsourcing Platforms - A Use-Case Driven Approach},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-14072},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-140726},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Computer systems have replaced human work-force in many parts of everyday life, but there still exists a large number of tasks that cannot be automated, yet. This also includes tasks, which we consider to be rather simple like the categorization of image content or subjective ratings. Traditionally, these tasks have been completed by designated employees or outsourced to specialized companies. However, recently the crowdsourcing paradigm is more and more applied to complete such human-labor intensive tasks. Crowdsourcing aims at leveraging the huge number of Internet users all around the globe, which form a potentially highly available, low-cost, and easy accessible work-force. To enable the distribution of work on a global scale, new web-based services emerged, so called crowdsourcing platforms, that act as mediator between employers posting tasks and workers completing tasks. However, the crowdsourcing approach, especially the large anonymous worker crowd, results in two types of challenges. On the one hand, there are technical challenges like the dimensioning of crowdsourcing platform infrastructure or the interconnection of crowdsourcing platforms and machine clouds to build hybrid services. On the other hand, there are conceptual challenges like identifying reliable workers or migrating traditional off-line work to the crowdsourcing environment. To tackle these challenges, this monograph analyzes and models current crowdsourcing systems to optimize crowdsourcing workflows and the underlying infrastructure. First, a categorization of crowdsourcing tasks and platforms is developed to derive generalizable properties. Based on this categorization and an exemplary analysis of a commercial crowdsourcing platform, models for different aspects of crowdsourcing platforms and crowdsourcing mechanisms are developed. A special focus is put on quality assurance mechanisms for crowdsourcing tasks, where the models are used to assess the suitability and costs of existing approaches for different types of tasks. Further, a novel quality assurance mechanism solely based on user-interactions is proposed and its feasibility is shown. The findings from the analysis of existing platforms, the derived models, and the developed quality assurance mechanisms are finally used to derive best practices for two crowdsourcing use-cases, crowdsourcing-based network measurements and crowdsourcing-based subjective user studies. These two exemplary use-cases cover aspects typical for a large range of crowdsourcing tasks and illustrated the potential benefits, but also resulting challenges when using crowdsourcing. With the ongoing digitalization and globalization of the labor markets, the crowdsourcing paradigm is expected to gain even more importance in the next years. This is already evident in the currently new emerging fields of crowdsourcing, like enterprise crowdsourcing or mobile crowdsourcing. The models developed in the monograph enable platform providers to optimize their current systems and employers to optimize their workflows to increase their commercial success. Moreover, the results help to improve the general understanding of crowdsourcing systems, a key for identifying necessary adaptions and future improvements.},
  subject      = {Open Innovation},
  language  = {en}
}