�ݺ�ߣshows by User: tobiashossfeld

�ݺ�ߣshows by User: tobiashossfeld / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: tobiashossfeld / Wed, 11 Jul 2018 13:22:12 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: tobiashossfeld QoE-driven Networking /slideshow/qoedriven-networking/105351794 tmaweb-180711132212
Quality of Experience (QoE) has received much attention over the past years and is defined as is the degree of delight or annoyance of the user of an application or service. For various stakeholders QoE has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modeling QoE for a variety of media services. In this talk, emerging challenges and concepts are discussed for managing QoE for networked media services. The following topics are addressed: multi-factor QoE modeling, QoE metrics and QoE fairness for QoE management, as well as recent efforts for web QoE monitoring. Finally, QoE++ is postulated, the evolution from the QoE ego-system towards the QoE eco-systems.]]>
Quality of Experience (QoE) has received much attention over the past years and is defined as is the degree of delight or annoyance of the user of an application or service. For various stakeholders QoE has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modeling QoE for a variety of media services. In this talk, emerging challenges and concepts are discussed for managing QoE for networked media services. The following topics are addressed: multi-factor QoE modeling, QoE metrics and QoE fairness for QoE management, as well as recent efforts for web QoE monitoring. Finally, QoE++ is postulated, the evolution from the QoE ego-system towards the QoE eco-systems.]]> Wed, 11 Jul 2018 13:22:12 GMT /slideshow/qoedriven-networking/105351794 tobiashossfeld@slideshare.net(tobiashossfeld) QoE-driven Networking tobiashossfeld Quality of Experience (QoE) has received much attention over the past years and is defined as is the degree of delight or annoyance of the user of an application or service. For various stakeholders QoE has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modeling QoE for a variety of media services. In this talk, emerging challenges and concepts are discussed for managing QoE for networked media services. The following topics are addressed: multi-factor QoE modeling, QoE metrics and QoE fairness for QoE management, as well as recent efforts for web QoE monitoring. Finally, QoE++ is postulated, the evolution from the QoE ego-system towards the QoE eco-systems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tmaweb-180711132212-thumbnail.jpg?width=120&height=120&fit=bounds" /> Quality of Experience (QoE) has received much attention over the past years and is defined as is the degree of delight or annoyance of the user of an application or service. For various stakeholders QoE has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modeling QoE for a variety of media services. In this talk, emerging challenges and concepts are discussed for managing QoE for networked media services. The following topics are addressed: multi-factor QoE modeling, QoE metrics and QoE fairness for QoE management, as well as recent efforts for web QoE monitoring. Finally, QoE++ is postulated, the evolution from the QoE ego-system towards the QoE eco-systems.

QoE-driven Networking from Tobias Hoテ歿eld

]]> 179 2 https://cdn.slidesharecdn.com/ss_thumbnails/tmaweb-180711132212-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Traffic Modeling for Aggregated Periodic IoT Data /slideshow/traffic-modeling-for-aggregated-periodic-iot-data/89296536 aggregatediottraffichossfeldweb-180301182157
The prevalence of IoT is driven by industrial requirements and scales, but also by community curiosity and tinkering in participatory crowdsensing endeavours. This tutorial first explores the practical requirements and options of modern IoT appliances and projects, including all aspects of the diverse stack, from PHY to application. With that as base, traffic models can now be derived and evaluated for these IoT topologies that might provide a better fit than traditional approaches. The slides discuss the second part dedicated to traffic modeling for Aggregated Periodic IoT Data.]]>
The prevalence of IoT is driven by industrial requirements and scales, but also by community curiosity and tinkering in participatory crowdsensing endeavours. This tutorial first explores the practical requirements and options of modern IoT appliances and projects, including all aspects of the diverse stack, from PHY to application. With that as base, traffic models can now be derived and evaluated for these IoT topologies that might provide a better fit than traditional approaches. The slides discuss the second part dedicated to traffic modeling for Aggregated Periodic IoT Data.]]> Thu, 01 Mar 2018 18:21:57 GMT /slideshow/traffic-modeling-for-aggregated-periodic-iot-data/89296536 tobiashossfeld@slideshare.net(tobiashossfeld) Traffic Modeling for Aggregated Periodic IoT Data tobiashossfeld The prevalence of IoT is driven by industrial requirements and scales, but also by community curiosity and tinkering in participatory crowdsensing endeavours. This tutorial first explores the practical requirements and options of modern IoT appliances and projects, including all aspects of the diverse stack, from PHY to application. With that as base, traffic models can now be derived and evaluated for these IoT topologies that might provide a better fit than traditional approaches. The slides discuss the second part dedicated to traffic modeling for Aggregated Periodic IoT Data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aggregatediottraffichossfeldweb-180301182157-thumbnail.jpg?width=120&height=120&fit=bounds" /> The prevalence of IoT is driven by industrial requirements and scales, but also by community curiosity and tinkering in participatory crowdsensing endeavours. This tutorial first explores the practical requirements and options of modern IoT appliances and projects, including all aspects of the diverse stack, from PHY to application. With that as base, traffic models can now be derived and evaluated for these IoT topologies that might provide a better fit than traditional approaches. The slides discuss the second part dedicated to traffic modeling for Aggregated Periodic IoT Data.

Traffic Modeling for Aggregated Periodic IoT Data from Tobias Hoテ歿eld

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0 On QoE Metrics and QoE Fairness for Network & Traffic Management /slideshow/on-qoe-metrics-and-qoe-fairness-for-network-traffic-management/67166998 across-bilbao-web-161014081932
*** QoE Metrics *** 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 Mean Opinion Scores (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. 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. *** QoE Fairness *** 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.]]>
*** QoE Metrics *** 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 Mean Opinion Scores (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. 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. *** QoE Fairness *** 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.]]> Fri, 14 Oct 2016 08:19:32 GMT /slideshow/on-qoe-metrics-and-qoe-fairness-for-network-traffic-management/67166998 tobiashossfeld@slideshare.net(tobiashossfeld) On QoE Metrics and QoE Fairness for Network & Traffic Management tobiashossfeld *** QoE Metrics *** 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 Mean Opinion Scores (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. 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. *** QoE Fairness *** 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/across-bilbao-web-161014081932-thumbnail.jpg?width=120&height=120&fit=bounds" /> *** QoE Metrics *** 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 Mean Opinion Scores (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. 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. *** QoE Fairness *** 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.

On QoE Metrics and QoE Fairness for Network & Traffic Management from Tobias Hoテ歿eld

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0 QoE++: Shifting from Ego- to Eco-System? QCMan 2015 Keynote /slideshow/hossfeld-qc-man2015keynoteweb/48151976 hossfeldqcman2015keynoteweb-150514151640-lva1-app6891
QoE++: Shifting from Ego- to Eco-System? QoE research has advanced significantly in recent years with a focus on the QoE ego-system. Thereby, different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE. However, in order to progress in the area of QoE, new research directions have to be taken. There is a need for QoE++. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user. In comparison to the traditional QoE ego-system thinking, the QoE eco-system addresses among others the following research topics: in-session vs. global system perspective, short- vs. long-time scales when considering QoE, single vs. multi-user QoE, single vs. concurrent usage of applications and services, user vs. business perspective by addressing all key stakeholder goals. QoE++ requires (a) to extend current QoE models by the different perspectives of the QoE eco-system including the service provider perspective, (b) to incorporate user behavior as part of the model, (c) and to identify and include relevant internal and external context factors including physical, cultural, social, economic context. QoE++ faces several major challenges. (1) Can we utilize QoE for network & service management? Or is it more appropriate to consider user engagement or user behavior? Which context factors are relevant or are such context-factors even more important for network & service management, e.g. in order to foresee and react on flash crowds? (2) How to realize cross-layer optimization between applications and their demands and the network capabilities, and thus a network-wide QoE optimization? Is SDN the right technology to cope with those challenges? (3) Can we transform QoE into business models, SLAs, etc.? Or is it possible to 'trade' QoE? For example, offering WiFi sharing at home, a user may get improved service delivery and QoE by its ISP. (4) Do we understand fundamental models and natural relationships of QoE++? How can we extend existing QoE models to take into account the service provider's perspective? How to quantify QoE fairness? What is the relationship between QoE and user behavior? Following QoE++ will shift from ego- to eco-systems and give answers to those questions. In this talk, we will discuss QoE++ and highlight some of the challenges above.]]>
QoE++: Shifting from Ego- to Eco-System? QoE research has advanced significantly in recent years with a focus on the QoE ego-system. Thereby, different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE. However, in order to progress in the area of QoE, new research directions have to be taken. There is a need for QoE++. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user. In comparison to the traditional QoE ego-system thinking, the QoE eco-system addresses among others the following research topics: in-session vs. global system perspective, short- vs. long-time scales when considering QoE, single vs. multi-user QoE, single vs. concurrent usage of applications and services, user vs. business perspective by addressing all key stakeholder goals. QoE++ requires (a) to extend current QoE models by the different perspectives of the QoE eco-system including the service provider perspective, (b) to incorporate user behavior as part of the model, (c) and to identify and include relevant internal and external context factors including physical, cultural, social, economic context. QoE++ faces several major challenges. (1) Can we utilize QoE for network & service management? Or is it more appropriate to consider user engagement or user behavior? Which context factors are relevant or are such context-factors even more important for network & service management, e.g. in order to foresee and react on flash crowds? (2) How to realize cross-layer optimization between applications and their demands and the network capabilities, and thus a network-wide QoE optimization? Is SDN the right technology to cope with those challenges? (3) Can we transform QoE into business models, SLAs, etc.? Or is it possible to 'trade' QoE? For example, offering WiFi sharing at home, a user may get improved service delivery and QoE by its ISP. (4) Do we understand fundamental models and natural relationships of QoE++? How can we extend existing QoE models to take into account the service provider's perspective? How to quantify QoE fairness? What is the relationship between QoE and user behavior? Following QoE++ will shift from ego- to eco-systems and give answers to those questions. In this talk, we will discuss QoE++ and highlight some of the challenges above.]]> Thu, 14 May 2015 15:16:40 GMT /slideshow/hossfeld-qc-man2015keynoteweb/48151976 tobiashossfeld@slideshare.net(tobiashossfeld) QoE++: Shifting from Ego- to Eco-System? QCMan 2015 Keynote tobiashossfeld QoE++: Shifting from Ego- to Eco-System? QoE research has advanced significantly in recent years with a focus on the QoE ego-system. Thereby, different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE. However, in order to progress in the area of QoE, new research directions have to be taken. There is a need for QoE++. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user. In comparison to the traditional QoE ego-system thinking, the QoE eco-system addresses among others the following research topics: in-session vs. global system perspective, short- vs. long-time scales when considering QoE, single vs. multi-user QoE, single vs. concurrent usage of applications and services, user vs. business perspective by addressing all key stakeholder goals. QoE++ requires (a) to extend current QoE models by the different perspectives of the QoE eco-system including the service provider perspective, (b) to incorporate user behavior as part of the model, (c) and to identify and include relevant internal and external context factors including physical, cultural, social, economic context. QoE++ faces several major challenges. (1) Can we utilize QoE for network & service management? Or is it more appropriate to consider user engagement or user behavior? Which context factors are relevant or are such context-factors even more important for network & service management, e.g. in order to foresee and react on flash crowds? (2) How to realize cross-layer optimization between applications and their demands and the network capabilities, and thus a network-wide QoE optimization? Is SDN the right technology to cope with those challenges? (3) Can we transform QoE into business models, SLAs, etc.? Or is it possible to 'trade' QoE? For example, offering WiFi sharing at home, a user may get improved service delivery and QoE by its ISP. (4) Do we understand fundamental models and natural relationships of QoE++? How can we extend existing QoE models to take into account the service provider's perspective? How to quantify QoE fairness? What is the relationship between QoE and user behavior? Following QoE++ will shift from ego- to eco-systems and give answers to those questions. In this talk, we will discuss QoE++ and highlight some of the challenges above. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hossfeldqcman2015keynoteweb-150514151640-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> QoE++: Shifting from Ego- to Eco-System? QoE research has advanced significantly in recent years with a focus on the QoE ego-system. Thereby, different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE. However, in order to progress in the area of QoE, new research directions have to be taken. There is a need for QoE++. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user. In comparison to the traditional QoE ego-system thinking, the QoE eco-system addresses among others the following research topics: in-session vs. global system perspective, short- vs. long-time scales when considering QoE, single vs. multi-user QoE, single vs. concurrent usage of applications and services, user vs. business perspective by addressing all key stakeholder goals. QoE++ requires (a) to extend current QoE models by the different perspectives of the QoE eco-system including the service provider perspective, (b) to incorporate user behavior as part of the model, (c) and to identify and include relevant internal and external context factors including physical, cultural, social, economic context. QoE++ faces several major challenges. (1) Can we utilize QoE for network & service management? Or is it more appropriate to consider user engagement or user behavior? Which context factors are relevant or are such context-factors even more important for network & service management, e.g. in order to foresee and react on flash crowds? (2) How to realize cross-layer optimization between applications and their demands and the network capabilities, and thus a network-wide QoE optimization? Is SDN the right technology to cope with those challenges? (3) Can we transform QoE into business models, SLAs, etc.? Or is it possible to 'trade' QoE? For example, offering WiFi sharing at home, a user may get improved service delivery and QoE by its ISP. (4) Do we understand fundamental models and natural relationships of QoE++? How can we extend existing QoE models to take into account the service provider's perspective? How to quantify QoE fairness? What is the relationship between QoE and user behavior? Following QoE++ will shift from ego- to eco-systems and give answers to those questions. In this talk, we will discuss QoE++ and highlight some of the challenges above.

QoE++: Shifting from Ego- to Eco-System? QCMan 2015 Keynote from Tobias Hoテ歿eld

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0 Hossfeld qc man2015_context_monitoring_web /slideshow/hossfeld-qc-man2015contextmonitoringweb/48151754 hossfeldqcman2015contextmonitoringweb-150514151219-lva1-app6892
Over the last decade or so, significant research has focused on defining Quality of Experience (QoE) of Multimedia Systems and identifying the key factors that collectively determine it. Some consensus thus exists as to the role of System Factors, Human Factors and Context Factors. In this paper, the notion of context is broadened to include information gleaned from simultaneous out-of-band channels, such as social network trend analytics, that can be used if interpreted in a timely manner, to help further optimise QoE. A case study involving simulation of HTTP adaptive streaming (HAS) and load balancing in a content distribution network (CDN) in a flash crowd scenario is presented with encouraging results.]]>
Over the last decade or so, significant research has focused on defining Quality of Experience (QoE) of Multimedia Systems and identifying the key factors that collectively determine it. Some consensus thus exists as to the role of System Factors, Human Factors and Context Factors. In this paper, the notion of context is broadened to include information gleaned from simultaneous out-of-band channels, such as social network trend analytics, that can be used if interpreted in a timely manner, to help further optimise QoE. A case study involving simulation of HTTP adaptive streaming (HAS) and load balancing in a content distribution network (CDN) in a flash crowd scenario is presented with encouraging results.]]> Thu, 14 May 2015 15:12:18 GMT /slideshow/hossfeld-qc-man2015contextmonitoringweb/48151754 tobiashossfeld@slideshare.net(tobiashossfeld) Hossfeld qc man2015_context_monitoring_web tobiashossfeld Over the last decade or so, significant research has focused on defining Quality of Experience (QoE) of Multimedia Systems and identifying the key factors that collectively determine it. Some consensus thus exists as to the role of System Factors, Human Factors and Context Factors. In this paper, the notion of context is broadened to include information gleaned from simultaneous out-of-band channels, such as social network trend analytics, that can be used if interpreted in a timely manner, to help further optimise QoE. A case study involving simulation of HTTP adaptive streaming (HAS) and load balancing in a content distribution network (CDN) in a flash crowd scenario is presented with encouraging results. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hossfeldqcman2015contextmonitoringweb-150514151219-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Over the last decade or so, significant research has focused on defining Quality of Experience (QoE) of Multimedia Systems and identifying the key factors that collectively determine it. Some consensus thus exists as to the role of System Factors, Human Factors and Context Factors. In this paper, the notion of context is broadened to include information gleaned from simultaneous out-of-band channels, such as social network trend analytics, that can be used if interpreted in a timely manner, to help further optimise QoE. A case study involving simulation of HTTP adaptive streaming (HAS) and load balancing in a content distribution network (CDN) in a flash crowd scenario is presented with encouraging results.

Hossfeld qc man2015_context_monitoring_web from Tobias Hoテ歿eld

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0 https://cdn.slidesharecdn.com/profile-photo-tobiashossfeld-48x48.jpg?cb=1553001095 http://www3.informatik.uni-wuerzburg.de/staff/hossfeld/ https://cdn.slidesharecdn.com/ss_thumbnails/tmaweb-180711132212-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/qoedriven-networking/105351794 QoE-driven Networking https://cdn.slidesharecdn.com/ss_thumbnails/aggregatediottraffichossfeldweb-180301182157-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/traffic-modeling-for-aggregated-periodic-iot-data/89296536 Traffic Modeling for A... https://cdn.slidesharecdn.com/ss_thumbnails/across-bilbao-web-161014081932-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/on-qoe-metrics-and-qoe-fairness-for-network-traffic-management/67166998 On QoE Metrics and QoE...