Dr. rer. nat. Susanna Schwarzmann

• Computing QoE-relevant Adaptive Video Streaming Metrics using Discrete-Time Analysis. Schwarzmann, Susanna; Breitbach, Paula; Zinner, Thomas. In Third International Workshop on Quality of Experience Management 2019. 2019.
  • [ BibTeX ]
  • [ EndNote ]
  • [ Download ]
  @inproceedings{info3-inproceedings-2019-3, author = {Schwarzmann, Susanna and Breitbach, Paula and Zinner, Thomas}, booktitle = {Third International Workshop on Quality of Experience Management 2019}, keywords = {dfg-sdn}, month = {02}, title = {Computing QoE-relevant Adaptive Video Streaming Metrics using Discrete-Time Analysis}, year = 2019 }

  %0 Conference Paper %1 info3-inproceedings-2019-3 %A Schwarzmann, Susanna %A Breitbach, Paula %A Zinner, Thomas %B Third International Workshop on Quality of Experience Management 2019 %D 2019 %T Computing QoE-relevant Adaptive Video Streaming Metrics using Discrete-Time Analysis

• Evaluation of the Benefits of Variable Segment Durations for Adaptive Streaming. Schwarzmann, Susanna; Zinner, Thomas; Sieber, Christian; Geissler, Stefan. In The 2nd International Workshop on Quality of Experience Management. Sardinia, Italy, 2018.
  • [ Abstract ]
  • [ BibTeX ]
  • [ EndNote ]
  • [ Download ]
  HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. It enables the dynamic adaptation of video quality by splitting the video clip into small segments and providing multiple quality levels per segment. Current HAS streaming services typically utilize segments of equal durations. However, this leads to video encoding overhead as segments have to start with I-frames, independently of the encoded video content. In this paper we evaluate the prospects of variable segment durations, where video segments are aligned to the video characteristics. We evaluate the reduction of the encoding overhead and investigate its impact on the stalling probability using a theoretical model. It turns out that the variable approach outperforms the fixed approach in 86% of the evaluated cases with respect to video stalls.

  @inproceedings{info3-inproceedings-2018-12, abstract = {HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. It enables the dynamic adaptation of video quality by splitting the video clip into small segments and providing multiple quality levels per segment. Current HAS streaming services typically utilize segments of equal durations. However, this leads to video encoding overhead as segments have to start with I-frames, independently of the encoded video content. In this paper we evaluate the prospects of variable segment durations, where video segments are aligned to the video characteristics. We evaluate the reduction of the encoding overhead and investigate its impact on the stalling probability using a theoretical model. It turns out that the variable approach outperforms the fixed approach in 86% of the evaluated cases with respect to video stalls.}, address = {Sardinia, Italy}, author = {Schwarzmann, Susanna and Zinner, Thomas and Sieber, Christian and Geissler, Stefan}, booktitle = {The 2nd International Workshop on Quality of Experience Management}, keywords = {dfg-sdn}, month = {05}, title = {Evaluation of the Benefits of Variable Segment Durations for Adaptive Streaming}, year = 2018 }

  %0 Conference Paper %1 info3-inproceedings-2018-12 %A Schwarzmann, Susanna %A Zinner, Thomas %A Sieber, Christian %A Geissler, Stefan %B The 2nd International Workshop on Quality of Experience Management %C Sardinia, Italy %D 2018 %T Evaluation of the Benefits of Variable Segment Durations for Adaptive Streaming %X HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. It enables the dynamic adaptation of video quality by splitting the video clip into small segments and providing multiple quality levels per segment. Current HAS streaming services typically utilize segments of equal durations. However, this leads to video encoding overhead as segments have to start with I-frames, independently of the encoded video content. In this paper we evaluate the prospects of variable segment durations, where video segments are aligned to the video characteristics. We evaluate the reduction of the encoding overhead and investigate its impact on the stalling probability using a theoretical model. It turns out that the variable approach outperforms the fixed approach in 86% of the evaluated cases with respect to video stalls.
• Big-Data Helps SDN to Improve Application Specific Quality of Service. Schwarzmann, Susanna; Blenk, Andreas; Dobrijevic, Ognjen; Jarschel, Michael; Hotho, Andreas; Zinner, Thomas; Wamser, Florian. In Big Data and Software Defined Networks. Institution of Engineering and Technology (IET), 2018.
  • [ Abstract ]
  • [ BibTeX ]
  • [ EndNote ]
  • [ Download ]
  This chapter first provides an outline of the current results in the domains of: (a) quality-of-service (QoS) / quality-of-experience (QoE) control and management (CaM) for real-time multimedia services that is supported by software-defined networking (SDN), and (b) big data analytics and methods that are used for QoS/QoE CaM. Then, three specific use case scenarios with respect to video streaming services are presented, so as to illustrate the expected benefits of incorporating big data analytics into SDN-based CaM for the purposes of improving or optimizing QoS/QoE. In the end, we describe our vision and a high-level view of an SDN-based architecture for QoS/QoE CaM that is enriched with big data analytics' functional blocks and summarize corresponding challenges.

  @incollection{info3-incollection-2018-2, abstract = {This chapter first provides an outline of the current results in the domains of: (a) quality-of-service (QoS) / quality-of-experience (QoE) control and management (CaM) for real-time multimedia services that is supported by software-defined networking (SDN), and (b) big data analytics and methods that are used for QoS/QoE CaM. Then, three specific use case scenarios with respect to video streaming services are presented, so as to illustrate the expected benefits of incorporating big data analytics into SDN-based CaM for the purposes of improving or optimizing QoS/QoE. In the end, we describe our vision and a high-level view of an SDN-based architecture for QoS/QoE CaM that is enriched with big data analytics' functional blocks and summarize corresponding challenges.}, author = {Schwarzmann, Susanna and Blenk, Andreas and Dobrijevic, Ognjen and Jarschel, Michael and Hotho, Andreas and Zinner, Thomas and Wamser, Florian}, booktitle = {Big Data and Software Defined Networks}, keywords = {dfg-sdn}, month = {03}, publisher = {Institution of Engineering and Technology (IET)}, title = {Big-Data Helps SDN to Improve Application Specific Quality of Service}, year = 2018 }

  %0 Book Section %1 info3-incollection-2018-2 %A Schwarzmann, Susanna %A Blenk, Andreas %A Dobrijevic, Ognjen %A Jarschel, Michael %A Hotho, Andreas %A Zinner, Thomas %A Wamser, Florian %B Big Data and Software Defined Networks %D 2018 %I Institution of Engineering and Technology (IET) %T Big-Data Helps SDN to Improve Application Specific Quality of Service %X This chapter first provides an outline of the current results in the domains of: (a) quality-of-service (QoS) / quality-of-experience (QoE) control and management (CaM) for real-time multimedia services that is supported by software-defined networking (SDN), and (b) big data analytics and methods that are used for QoS/QoE CaM. Then, three specific use case scenarios with respect to video streaming services are presented, so as to illustrate the expected benefits of incorporating big data analytics into SDN-based CaM for the purposes of improving or optimizing QoS/QoE. In the end, we describe our vision and a high-level view of an SDN-based architecture for QoS/QoE CaM that is enriched with big data analytics' functional blocks and summarize corresponding challenges.
• QoE Management for Future Networks. Schatz, Raimund; Schwarzmann, Susanna; Zinner, Thomas; Dobrijevic, Ognjen; Liotou, Eirini; Pocta, Peter; Baraković, Sabina; Baraković Husić, Jasmina; Skorin-Kapov, Lea. In Autonomous Control for a Reliable Internet of Services: Methods, Models, Approaches, Techniques, Algorithms and Tools. Springer, 2018.
  • [ Abstract ]
  • [ BibTeX ]
  • [ EndNote ]
  This chapter discusses prospects of QoE management for future networks and applications. After motivating QoE management, it first provides an introduction to the concept by discussing its origins, key terms and giving an overview of the most relevant existing theoretical frameworks. Then, recent research on promising technical approaches to QoE- driven management that operate across different layers of the networking stack is discussed. Finally, the chapter provides conclusions and an outlook on the future of QoE management with a focus on those key enablers (including cooperation, business models and key technologies) that are essential for ultimately turning QoE-aware network and application management into reality.

  @incollection{info3-incollection-2018-1, abstract = {This chapter discusses prospects of QoE management for future networks and applications. After motivating QoE management, it first provides an introduction to the concept by discussing its origins, key terms and giving an overview of the most relevant existing theoretical frameworks. Then, recent research on promising technical approaches to QoE- driven management that operate across different layers of the networking stack is discussed. Finally, the chapter provides conclusions and an outlook on the future of QoE management with a focus on those key enablers (including cooperation, business models and key technologies) that are essential for ultimately turning QoE-aware network and application management into reality.}, author = {Schatz, Raimund and Schwarzmann, Susanna and Zinner, Thomas and Dobrijevic, Ognjen and Liotou, Eirini and Pocta, Peter and Baraković, Sabina and Baraković Husić, Jasmina and Skorin-Kapov, Lea}, booktitle = {Autonomous Control for a Reliable Internet of Services: Methods, Models, Approaches, Techniques, Algorithms and Tools}, keywords = {ngn}, publisher = {Springer}, title = {QoE Management for Future Networks}, year = 2018 }

  %0 Book Section %1 info3-incollection-2018-1 %A Schatz, Raimund %A Schwarzmann, Susanna %A Zinner, Thomas %A Dobrijevic, Ognjen %A Liotou, Eirini %A Pocta, Peter %A Baraković, Sabina %A Baraković Husić, Jasmina %A Skorin-Kapov, Lea %B Autonomous Control for a Reliable Internet of Services: Methods, Models, Approaches, Techniques, Algorithms and Tools %D 2018 %I Springer %T QoE Management for Future Networks %X This chapter discusses prospects of QoE management for future networks and applications. After motivating QoE management, it first provides an introduction to the concept by discussing its origins, key terms and giving an overview of the most relevant existing theoretical frameworks. Then, recent research on promising technical approaches to QoE- driven management that operate across different layers of the networking stack is discussed. Finally, the chapter provides conclusions and an outlook on the future of QoE management with a focus on those key enablers (including cooperation, business models and key technologies) that are essential for ultimately turning QoE-aware network and application management into reality.

• Quantitative Comparison of Application-Network Interaction: A Case Study of Adaptive Video Streaming. Schwarzmann, Susanna; Zinner, Thomas; Dobrijevic, Ognjen. In Springer Quality and User Experience. 2017.
  • [ Abstract ]
  • [ BibTeX ]
  • [ EndNote ]
  Managing quality of experience (QoE) is now widely accepted as a critical objective for multimedia applications and the supporting communication systems. In general, QoE management encompasses: (i) monitoring of the key influence factors and QoE indicators, and (ii) deciding on the appropriate control actions as specified by the management goal. Many multimedia applications, e.g. video streaming and audio conferencing, are able to adjust their operational parameters so as to react to variations in the network performance. However, such an adaptation feature is mostly based on a local client view of the network conditions, which may lead to an unfair allocation of network resources among heterogeneous clients and, thus, an unfair QoE distribution. In order to tackle this issue, there is the call for a cooperation between the applications and the underlying network, which includes application-network interaction (App-Net) in terms of: (1) exchanging information on the monitored QoE indicators, and (2) coordinating the QoE control actions. Various App-Net mechanisms focusing on specific use cases and applications have been proposed to date. This paper gives an overview of App-Net mechanisms and proposes a generic App-Net model that provides the means to realize a coordinated QoE-centric management. Based on the App-Net model, we develop an evaluation methodology to compare three App-Net mechanisms for managing QoE of HTTP adaptive streaming (HAS) against a baseline HAS service. The aim of this quantitative comparison is to explore the trade-offs between QoE gains and the complexity of App-Net implementation, with respect to the number of monitoring and control messages, achieved video quality, and QoE fairness among heterogeneous clients. Our ultimate goal is to set up reproducible experiments that facilitate a holistic evaluation of different App-Net mechanisms.

  @article{info3-article-2017-4, abstract = {Managing quality of experience (QoE) is now widely accepted as a critical objective for multimedia applications and the supporting communication systems. In general, QoE management encompasses: (i) monitoring of the key influence factors and QoE indicators, and (ii) deciding on the appropriate control actions as specified by the management goal. Many multimedia applications, e.g. video streaming and audio conferencing, are able to adjust their operational parameters so as to react to variations in the network performance. However, such an adaptation feature is mostly based on a local client view of the network conditions, which may lead to an unfair allocation of network resources among heterogeneous clients and, thus, an unfair QoE distribution. In order to tackle this issue, there is the call for a cooperation between the applications and the underlying network, which includes application-network interaction (App-Net) in terms of: (1) exchanging information on the monitored QoE indicators, and (2) coordinating the QoE control actions. Various App-Net mechanisms focusing on specific use cases and applications have been proposed to date. This paper gives an overview of App-Net mechanisms and proposes a generic App-Net model that provides the means to realize a coordinated QoE-centric management. Based on the App-Net model, we develop an evaluation methodology to compare three App-Net mechanisms for managing QoE of HTTP adaptive streaming (HAS) against a baseline HAS service. The aim of this quantitative comparison is to explore the trade-offs between QoE gains and the complexity of App-Net implementation, with respect to the number of monitoring and control messages, achieved video quality, and QoE fairness among heterogeneous clients. Our ultimate goal is to set up reproducible experiments that facilitate a holistic evaluation of different App-Net mechanisms.}, author = {Schwarzmann, Susanna and Zinner, Thomas and Dobrijevic, Ognjen}, journal = {Springer Quality and User Experience}, keywords = {sys:hidden:article}, title = {Quantitative Comparison of Application-Network Interaction: A Case Study of Adaptive Video Streaming}, year = 2017 }

  %0 Journal Article %1 info3-article-2017-4 %A Schwarzmann, Susanna %A Zinner, Thomas %A Dobrijevic, Ognjen %D 2017 %J Springer Quality and User Experience %T Quantitative Comparison of Application-Network Interaction: A Case Study of Adaptive Video Streaming %X Managing quality of experience (QoE) is now widely accepted as a critical objective for multimedia applications and the supporting communication systems. In general, QoE management encompasses: (i) monitoring of the key influence factors and QoE indicators, and (ii) deciding on the appropriate control actions as specified by the management goal. Many multimedia applications, e.g. video streaming and audio conferencing, are able to adjust their operational parameters so as to react to variations in the network performance. However, such an adaptation feature is mostly based on a local client view of the network conditions, which may lead to an unfair allocation of network resources among heterogeneous clients and, thus, an unfair QoE distribution. In order to tackle this issue, there is the call for a cooperation between the applications and the underlying network, which includes application-network interaction (App-Net) in terms of: (1) exchanging information on the monitored QoE indicators, and (2) coordinating the QoE control actions. Various App-Net mechanisms focusing on specific use cases and applications have been proposed to date. This paper gives an overview of App-Net mechanisms and proposes a generic App-Net model that provides the means to realize a coordinated QoE-centric management. Based on the App-Net model, we develop an evaluation methodology to compare three App-Net mechanisms for managing QoE of HTTP adaptive streaming (HAS) against a baseline HAS service. The aim of this quantitative comparison is to explore the trade-offs between QoE gains and the complexity of App-Net implementation, with respect to the number of monitoring and control messages, achieved video quality, and QoE fairness among heterogeneous clients. Our ultimate goal is to set up reproducible experiments that facilitate a holistic evaluation of different App-Net mechanisms.

• Towards a Framework for Comparing QoE-Centric Mechanisms of Application-Network Interaction. Schwarzmann, Susanna; Zinner, Thomas; Dobrijevic, Ognjen. In Proceedings of the 4th International IEEE Workshop on Quality of Experience Centric Management (2016). Würzburg, 2016.
  • [ BibTeX ]
  • [ EndNote ]
  @inproceedings{info3-inproceedings-2015-26, address = {Würzburg}, author = {Schwarzmann, Susanna and Zinner, Thomas and Dobrijevic, Ognjen}, booktitle = {Proceedings of the 4th International IEEE Workshop on Quality of Experience Centric Management (2016)}, keywords = {dfg-sdn}, month = {09}, title = {Towards a Framework for Comparing QoE-Centric Mechanisms of Application-Network Interaction}, year = 2016 }

  %0 Conference Paper %1 info3-inproceedings-2015-26 %A Schwarzmann, Susanna %A Zinner, Thomas %A Dobrijevic, Ognjen %B Proceedings of the 4th International IEEE Workshop on Quality of Experience Centric Management (2016) %C Würzburg %D 2016 %T Towards a Framework for Comparing QoE-Centric Mechanisms of Application-Network Interaction

• Text Categorization for Deriving the Application Quality in Enterprises using Ticketing Systems. Zinner, Thomas; Lemmerich, Florian; Schwarzmann, Susanna; Hirth, Matthias; Karg, Peter; Hotho, Andreas. In 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015). Valencia, Spain, 2015.
  • [ Abstract ]
  • [ BibTeX ]
  • [ EndNote ]
  • [ Download ]
  Today’s enterprise services and business applications are of- ten centralized in a small number of data centers. Employees located at branches and side offices access the computing infrastructure via the internet using thin client architectures. The task to provide a good application quality to the employers using a multitude of different applications and access networks has thus become complex. Enterprises have to be able to identify resource bottlenecks and applications with a poor performance quickly to take appropriate countermeasures and enable a good application quality for their employees. Ticketing systems within an enterprise use large databases for collecting complaints and problems of the users over a long period of time and thus are an interesting starting point to identify performance problems. However, manual categorization of tickets comes with a high workload. In this paper, we analyze in a case study the applicability of supervised learning algorithms for the automatic identification of relevant tickets, i.e., tickets indicating problematic applications. In that regard, we evaluate different classification algorithms using 12,000 manually annotated tickets accumulated in July 2013 at the ticketing system of a nation- wide operating enterprise. In addition to traditional machine learning metrics, we also analyze the performance of the different classifiers on business-relevant metrics.

  @inproceedings{info3-inproceedings-2015-517, abstract = {Today’s enterprise services and business applications are of- ten centralized in a small number of data centers. Employees located at branches and side offices access the computing infrastructure via the internet using thin client architectures. The task to provide a good application quality to the employers using a multitude of different applications and access networks has thus become complex. Enterprises have to be able to identify resource bottlenecks and applications with a poor performance quickly to take appropriate countermeasures and enable a good application quality for their employees. Ticketing systems within an enterprise use large databases for collecting complaints and problems of the users over a long period of time and thus are an interesting starting point to identify performance problems. However, manual categorization of tickets comes with a high workload. In this paper, we analyze in a case study the applicability of supervised learning algorithms for the automatic identification of relevant tickets, i.e., tickets indicating problematic applications. In that regard, we evaluate different classification algorithms using 12,000 manually annotated tickets accumulated in July 2013 at the ticketing system of a nation- wide operating enterprise. In addition to traditional machine learning metrics, we also analyze the performance of the different classifiers on business-relevant metrics.}, address = {Valencia, Spain}, author = {Zinner, Thomas and Lemmerich, Florian and Schwarzmann, Susanna and Hirth, Matthias and Karg, Peter and Hotho, Andreas}, booktitle = {17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015)}, keywords = {sys:hidden:inproceedings}, month = {09}, title = {Text Categorization for Deriving the Application Quality in Enterprises using Ticketing Systems}, year = 2015 }

  %0 Conference Paper %1 info3-inproceedings-2015-517 %A Zinner, Thomas %A Lemmerich, Florian %A Schwarzmann, Susanna %A Hirth, Matthias %A Karg, Peter %A Hotho, Andreas %B 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015) %C Valencia, Spain %D 2015 %T Text Categorization for Deriving the Application Quality in Enterprises using Ticketing Systems %X Today’s enterprise services and business applications are of- ten centralized in a small number of data centers. Employees located at branches and side offices access the computing infrastructure via the internet using thin client architectures. The task to provide a good application quality to the employers using a multitude of different applications and access networks has thus become complex. Enterprises have to be able to identify resource bottlenecks and applications with a poor performance quickly to take appropriate countermeasures and enable a good application quality for their employees. Ticketing systems within an enterprise use large databases for collecting complaints and problems of the users over a long period of time and thus are an interesting starting point to identify performance problems. However, manual categorization of tickets comes with a high workload. In this paper, we analyze in a case study the applicability of supervised learning algorithms for the automatic identification of relevant tickets, i.e., tickets indicating problematic applications. In that regard, we evaluate different classification algorithms using 12,000 manually annotated tickets accumulated in July 2013 at the ticketing system of a nation- wide operating enterprise. In addition to traditional machine learning metrics, we also analyze the performance of the different classifiers on business-relevant metrics.

• Deriving the Employee-perceived Application Quality in Enterprise IT Infrastructures using Information from Ticketing Systems. Schwarzmann, Susanna; Zinner, Thomas; Hirth, Matthias. In Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML 2014). Aachen, Germany, 2014.
  • [ BibTeX ]
  • [ EndNote ]
  • [ Download ]
  @inproceedings{info3-inproceedings-2014-502, address = {Aachen, Germany}, author = {Schwarzmann, Susanna and Zinner, Thomas and Hirth, Matthias}, booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML 2014)}, keywords = {sys:hidden:inproceedings}, month = {09}, title = {Deriving the Employee-perceived Application Quality in Enterprise IT Infrastructures using Information from Ticketing Systems}, year = 2014 }

  %0 Conference Paper %1 info3-inproceedings-2014-502 %A Schwarzmann, Susanna %A Zinner, Thomas %A Hirth, Matthias %B Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML 2014) %C Aachen, Germany %D 2014 %T Deriving the Employee-perceived Application Quality in Enterprise IT Infrastructures using Information from Ticketing Systems