Frank Loh M. Sc.

2021

1.
Pimpinella, A., Redondi, A.E., Loh, F., Seufert, M.: Machine-Learning Based Prediction of Next HTTP Request Arrival Time in Adaptive Video Streaming. 3rd International Workshop on High-Precision, Predictable, and Low-Latency Networking (HiPNet). , Izmir, Turkey (Virtual Conference) (2021).
[ BibTeX ] [ EndNote ]
 
@inproceedings{info3-inproceedings-2021-18, address = {Izmir, Turkey (Virtual Conference)}, author = {Pimpinella, Andrea and Redondi, Alessandro E.C. and Loh, Frank and Seufert, Michael}, booktitle = {3rd International Workshop on High-Precision, Predictable, and Low-Latency Networking (HiPNet)}, keywords = {myown sys:hidden:inproceedings ucn}, month = 10, title = {Machine-Learning Based Prediction of Next HTTP Request Arrival Time in Adaptive Video Streaming}, year = 2021 }
%0 Conference Paper %1 info3-inproceedings-2021-18 %A Pimpinella, Andrea %A Redondi, Alessandro E.C. %A Loh, Frank %A Seufert, Michael %B 3rd International Workshop on High-Precision, Predictable, and Low-Latency Networking (HiPNet) %C Izmir, Turkey (Virtual Conference) %D 2021 %T Machine-Learning Based Prediction of Next HTTP Request Arrival Time in Adaptive Video Streaming
2.
Loh, F., Poignée, F., Wamser, F., Leidinger, F., Hoßfeld, T.: Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Sensors. 21, 4172 (2021).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.
@article{info3-article-2021-3, abstract = {Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.}, author = {Loh, Frank and Poignée, Fabian and Wamser, Florian and Leidinger, Ferdinand and Hoßfeld, Tobias}, journal = {Sensors}, keywords = {youtube myown sys:hidden:article can}, number = 12, pages = 4172, title = {Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming}, volume = 21, year = 2021 }
%0 Journal Article %1 info3-article-2021-3 %A Loh, Frank %A Poignée, Fabian %A Wamser, Florian %A Leidinger, Ferdinand %A Hoßfeld, Tobias %D 2021 %J Sensors %N 12 %P 4172 %T Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming %V 21 %X Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.
3.
Loh, F., Geißler, S., Schaible, F., Hoßfeld, T.: Talk to Me: Investigating the Traffic Characteristics of Amazon Echo Dot and Google Home. International Conference on Communications and Electronics, ICCE (2021).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
The application area of smart speakers is steadily increasing with the Amazon Echo family and Google Home being brand leaders. Use cases include, among others, updating the calendar, home automation, or simply assisting users in their every day life. With the increasing amount of devices, the traffic generation and requesting process becomes a relevant subject to be studied in order to create traffic models and predict the impact on networks by these types of devices. Furthermore, with a detailed understanding of the devices, service quality can be monitored and improved for the end user. For that reason, in this work the requesting and traffic generation process of the Amazon Echo Dot and Google Home are studied and the generated network load of both devices is compared. With the insights of this study and additional device usage statistics, detailed traffic and usage models can be created.
@inproceedings{info3-inproceedings-2021-5, abstract = {The application area of smart speakers is steadily increasing with the Amazon Echo family and Google Home being brand leaders. Use cases include, among others, updating the calendar, home automation, or simply assisting users in their every day life. With the increasing amount of devices, the traffic generation and requesting process becomes a relevant subject to be studied in order to create traffic models and predict the impact on networks by these types of devices. Furthermore, with a detailed understanding of the devices, service quality can be monitored and improved for the end user. For that reason, in this work the requesting and traffic generation process of the Amazon Echo Dot and Google Home are studied and the generated network load of both devices is compared. With the insights of this study and additional device usage statistics, detailed traffic and usage models can be created.}, author = {Loh, Frank and Geißler, Stefan and Schaible, Fabian and Hoßfeld, Tobias}, booktitle = {International Conference on Communications and Electronics, ICCE}, keywords = {myown sys:hidden:inproceedings}, month = 1, title = {Talk to Me: Investigating the Traffic Characteristics of Amazon Echo Dot and Google Home}, year = 2021 }
%0 Conference Paper %1 info3-inproceedings-2021-5 %A Loh, Frank %A Geißler, Stefan %A Schaible, Fabian %A Hoßfeld, Tobias %B International Conference on Communications and Electronics, ICCE %D 2021 %T Talk to Me: Investigating the Traffic Characteristics of Amazon Echo Dot and Google Home %X The application area of smart speakers is steadily increasing with the Amazon Echo family and Google Home being brand leaders. Use cases include, among others, updating the calendar, home automation, or simply assisting users in their every day life. With the increasing amount of devices, the traffic generation and requesting process becomes a relevant subject to be studied in order to create traffic models and predict the impact on networks by these types of devices. Furthermore, with a detailed understanding of the devices, service quality can be monitored and improved for the end user. For that reason, in this work the requesting and traffic generation process of the Amazon Echo Dot and Google Home are studied and the generated network load of both devices is compared. With the insights of this study and additional device usage statistics, detailed traffic and usage models can be created.

2020

1.
Loh, F., Wamser, F., Moldovan, C., Zeidler, B., Tsilimantos, D., Valentin, S., Hoßfeld, T.: Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic. 32th IEEE/IFIP Network Operations and Management Symposium. , Budapest, Hungary (2020).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Today’s traffic projections speak of almost 58 % video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50 % encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.
@inproceedings{info3-inproceedings-2020-2, abstract = {Today’s traffic projections speak of almost 58 % video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50 % encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.}, address = {Budapest, Hungary}, author = {Loh, Frank and Wamser, Florian and Moldovan, Christian and Zeidler, Bernd and Tsilimantos, Dimitrios and Valentin, Stefan and Hoßfeld, Tobias}, booktitle = {32th IEEE/IFIP Network Operations and Management Symposium}, keywords = {huawei myown sys:hidden:inproceedings can}, month = 4, title = {Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic}, year = 2020 }
%0 Conference Paper %1 info3-inproceedings-2020-2 %A Loh, Frank %A Wamser, Florian %A Moldovan, Christian %A Zeidler, Bernd %A Tsilimantos, Dimitrios %A Valentin, Stefan %A Hoßfeld, Tobias %B 32th IEEE/IFIP Network Operations and Management Symposium %C Budapest, Hungary %D 2020 %T Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic %X Today’s traffic projections speak of almost 58 % video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50 % encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.
2.
Moldovan, C., Loh, F., Seufert, M., Hoßfeld, T.: Optimizing HAS for 360-Degree Videos. 5th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet). , Budapest, Hungary (2020).
[ BibTeX ] [ EndNote ]
 
@inproceedings{info3-inproceedings-2020-1, address = {Budapest, Hungary}, author = {Moldovan, Christian and Loh, Frank and Seufert, Michael and Hoßfeld, Tobias}, booktitle = {5th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet)}, keywords = {myown sys:hidden:inproceedings net-man}, month = 4, title = {Optimizing HAS for 360-Degree Videos}, year = 2020 }
%0 Conference Paper %1 info3-inproceedings-2020-1 %A Moldovan, Christian %A Loh, Frank %A Seufert, Michael %A Hoßfeld, Tobias %B 5th IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet) %C Budapest, Hungary %D 2020 %T Optimizing HAS for 360-Degree Videos

2019

1.
Loh, F., Vomhoff, V., Wamser, F., Metzger, F., Hoßfeld, T.: Traffic Measurement Study on Video Streaming with the Amazon Echo Show. 4th Internet-QoE Workshop: QoE-based Analysis and Management of Data Communication Networks (2019).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
The Amazon Echo Show is one of the most widely used smart speakers with the ability to stream video. Due to its popularity, the traffc profiles of such devices are of interest to network operators and providers. This work presents a measurement study of the Amazon Echo Show in terms of network traffc and streaming behavior. More than 470 hours of streaming data are collected and analyzed at network layer. Based on this, streaming quality is derived at application layer. The study quantifies the traffc and shows that streaming with the Amazon Echo Show is comparable to streaming with a native web browser, but in a more conservative way.
@inproceedings{info3-inproceedings-2019-26, abstract = {The Amazon Echo Show is one of the most widely used smart speakers with the ability to stream video. Due to its popularity, the traffc profiles of such devices are of interest to network operators and providers. This work presents a measurement study of the Amazon Echo Show in terms of network traffc and streaming behavior. More than 470 hours of streaming data are collected and analyzed at network layer. Based on this, streaming quality is derived at application layer. The study quantifies the traffc and shows that streaming with the Amazon Echo Show is comparable to streaming with a native web browser, but in a more conservative way.}, author = {Loh, Frank and Vomhoff, Viktoria and Wamser, Florian and Metzger, Florian and Hoßfeld, Tobias}, booktitle = {4th Internet-QoE Workshop: QoE-based Analysis and Management of Data Communication Networks}, keywords = {huawei myown sys:hidden:inproceedings can}, month = 10, title = {Traffic Measurement Study on Video Streaming with the Amazon Echo Show}, year = 2019 }
%0 Conference Paper %1 info3-inproceedings-2019-26 %A Loh, Frank %A Vomhoff, Viktoria %A Wamser, Florian %A Metzger, Florian %A Hoßfeld, Tobias %B 4th Internet-QoE Workshop: QoE-based Analysis and Management of Data Communication Networks %D 2019 %T Traffic Measurement Study on Video Streaming with the Amazon Echo Show %X The Amazon Echo Show is one of the most widely used smart speakers with the ability to stream video. Due to its popularity, the traffc profiles of such devices are of interest to network operators and providers. This work presents a measurement study of the Amazon Echo Show in terms of network traffc and streaming behavior. More than 470 hours of streaming data are collected and analyzed at network layer. Based on this, streaming quality is derived at application layer. The study quantifies the traffc and shows that streaming with the Amazon Echo Show is comparable to streaming with a native web browser, but in a more conservative way.
2.
Loh, F., Wamser, F., Moldovan, C., Zeidler, B., Hoßfeld, T., Tsilimantos, D., Valentin, S.: From click to playback: a dataset to study the response time of mobile YouTube. Proceedings of the 10th ACM Multimedia Systems Conference. pp. 267–272 (2019).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available.
@inproceedings{info3-inproceedings-2019-25, abstract = {Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available.}, author = {Loh, Frank and Wamser, Florian and Moldovan, Christian and Zeidler, Bernd and Hoßfeld, Tobias and Tsilimantos, Dimitrios and Valentin, Stefan}, booktitle = {Proceedings of the 10th ACM Multimedia Systems Conference}, keywords = {huawei myown sys:hidden:inproceedings can}, month = 6, pages = {267-272}, title = {From click to playback: a dataset to study the response time of mobile YouTube}, year = 2019 }
%0 Conference Paper %1 info3-inproceedings-2019-25 %A Loh, Frank %A Wamser, Florian %A Moldovan, Christian %A Zeidler, Bernd %A Hoßfeld, Tobias %A Tsilimantos, Dimitrios %A Valentin, Stefan %B Proceedings of the 10th ACM Multimedia Systems Conference %D 2019 %P 267-272 %T From click to playback: a dataset to study the response time of mobile YouTube %X Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available.

2018

1.
Karagkioules, T., Tsilimantos, D., Valentin, S., Wamser, F., Zeidler, B., Seufert, M., Loh, F., Tran-Gia, P.: A Public Dataset for YouTube’s Mobile Streaming Client. Network Traffic Measurement and Analysis Conference (TMA). , Vienna, Austria (2018).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{info3-inproceedings-2018-41, address = {Vienna, Austria}, author = {Karagkioules, Theodoros and Tsilimantos, Dimitrios and Valentin, Stefan and Wamser, Florian and Zeidler, Bernd and Seufert, Michael and Loh, Frank and Tran-Gia, Phuoc}, booktitle = {Network Traffic Measurement and Analysis Conference (TMA)}, keywords = {huawei myown sys:hidden:inproceedings can}, title = {A Public Dataset for YouTube’s Mobile Streaming Client}, year = 2018 }
%0 Conference Paper %1 info3-inproceedings-2018-41 %A Karagkioules, Theodoros %A Tsilimantos, Dimitrios %A Valentin, Stefan %A Wamser, Florian %A Zeidler, Bernd %A Seufert, Michael %A Loh, Frank %A Tran-Gia, Phuoc %B Network Traffic Measurement and Analysis Conference (TMA) %C Vienna, Austria %D 2018 %T A Public Dataset for YouTube’s Mobile Streaming Client
2.
Loh, F., Wamser, F., Hoßfeld, T., Tran-Gia, P.: Quality of Service Assessment of Live Video Streaming with a Remote-Controlled Drone. IEEE Conference on Network Softwarization. , Montreal, Canada (2018).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
Today's networks require a deep understanding of applications to optimize networks, efficiently design networks, and meet traffic demands, application heterogeneity, and application requirements. Current application areas include live video streaming and real-time applications, such as those that are named in 5G use cases with automation, disaster recovery, gaming, and Industry 4.0. In this work, we examine an application scenario with live video streaming and parallel real-time requirements in the uplink for disaster recovery. We study the quality of service (QoS) features of a remote-controlled drone. The drone is controlled via a tablet or smartphone while the video from the camera is transmitted from the drone to the user. There are high demands in both the uplink and downlink direction. The contribution of the work is the measurement of the QoS and application parameters for this scenario and the definition of influencing parameters for the application-layer.
@inproceedings{info3-inproceedings-2018-31, abstract = {Today's networks require a deep understanding of applications to optimize networks, efficiently design networks, and meet traffic demands, application heterogeneity, and application requirements. Current application areas include live video streaming and real-time applications, such as those that are named in 5G use cases with automation, disaster recovery, gaming, and Industry 4.0. In this work, we examine an application scenario with live video streaming and parallel real-time requirements in the uplink for disaster recovery. We study the quality of service (QoS) features of a remote-controlled drone. The drone is controlled via a tablet or smartphone while the video from the camera is transmitted from the drone to the user. There are high demands in both the uplink and downlink direction. The contribution of the work is the measurement of the QoS and application parameters for this scenario and the definition of influencing parameters for the application-layer.}, address = {Montreal, Canada}, author = {Loh, Frank and Wamser, Florian and Hoßfeld, Tobias and Tran-Gia, Phuoc}, booktitle = {IEEE Conference on Network Softwarization}, keywords = {myown input sys:hidden:inproceedings can}, month = 7, title = {Quality of Service Assessment of Live Video Streaming with a Remote-Controlled Drone}, year = 2018 }
%0 Conference Paper %1 info3-inproceedings-2018-31 %A Loh, Frank %A Wamser, Florian %A Hoßfeld, Tobias %A Tran-Gia, Phuoc %B IEEE Conference on Network Softwarization %C Montreal, Canada %D 2018 %T Quality of Service Assessment of Live Video Streaming with a Remote-Controlled Drone %X Today's networks require a deep understanding of applications to optimize networks, efficiently design networks, and meet traffic demands, application heterogeneity, and application requirements. Current application areas include live video streaming and real-time applications, such as those that are named in 5G use cases with automation, disaster recovery, gaming, and Industry 4.0. In this work, we examine an application scenario with live video streaming and parallel real-time requirements in the uplink for disaster recovery. We study the quality of service (QoS) features of a remote-controlled drone. The drone is controlled via a tablet or smartphone while the video from the camera is transmitted from the drone to the user. There are high demands in both the uplink and downlink direction. The contribution of the work is the measurement of the QoS and application parameters for this scenario and the definition of influencing parameters for the application-layer.
3.
Seufert, M., Zeidler, B., Wamser, F., Karagkioules, T., Tsilimantos, D., Loh, F., Tran-Gia, P., Valentin, S.: A Wrapper for Automatic Measurements with YouTube’s Native Android App. Network Traffic Measurement and Analysis Conference (TMA). , Vienna, Austria (2018).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
YouTube is one of the most popular and demanding services in the Internet today. Thereby, a large portion of this traffic is generated by YouTube's mobile app. While past studies have shown how to monitor browser-based streaming on desktop PCs (e.g., YoMo) or mobile devices (e.g., YoMoApp), streaming in the native app has not been monitored yet. This paper presents an automated framework for monitoring the streaming in YouTube's native app for Android. The concept is based on a wrapper application and the Android Debug Bridge (adb), and can be also extended to automatic measurements with other apps. For YouTube, it allows to collect application-layer streaming data, such as current playtime, buffered playtime, video encoding, and quality switches. These data can be complemented with network measurements on the mobile access link to obtain a holistic view on mobile YouTube streaming on Android devices. In addition to describing the software design and testbed setup, this paper discusses an experimental measurement. This study analyzes the streaming in the native YouTube app and compares it to the streaming from the mobile YouTube website via YoMoApp.
@inproceedings{info3-article-2018-4, abstract = {YouTube is one of the most popular and demanding services in the Internet today. Thereby, a large portion of this traffic is generated by YouTube's mobile app. While past studies have shown how to monitor browser-based streaming on desktop PCs (e.g., YoMo) or mobile devices (e.g., YoMoApp), streaming in the native app has not been monitored yet. This paper presents an automated framework for monitoring the streaming in YouTube's native app for Android. The concept is based on a wrapper application and the Android Debug Bridge (adb), and can be also extended to automatic measurements with other apps. For YouTube, it allows to collect application-layer streaming data, such as current playtime, buffered playtime, video encoding, and quality switches. These data can be complemented with network measurements on the mobile access link to obtain a holistic view on mobile YouTube streaming on Android devices. In addition to describing the software design and testbed setup, this paper discusses an experimental measurement. This study analyzes the streaming in the native YouTube app and compares it to the streaming from the mobile YouTube website via YoMoApp.}, address = {Vienna, Austria}, author = {Seufert, Michael and Zeidler, Bernd and Wamser, Florian and Karagkioules, Theodoros and Tsilimantos, Dimitrios and Loh, Frank and Tran-Gia, Phuoc and Valentin, Stefan}, booktitle = {Network Traffic Measurement and Analysis Conference (TMA)}, keywords = {youtube myown sys:hidden:inproceedings can}, month = 4, title = {A Wrapper for Automatic Measurements with YouTube's Native Android App}, year = 2018 }
%0 Conference Paper %1 info3-article-2018-4 %A Seufert, Michael %A Zeidler, Bernd %A Wamser, Florian %A Karagkioules, Theodoros %A Tsilimantos, Dimitrios %A Loh, Frank %A Tran-Gia, Phuoc %A Valentin, Stefan %B Network Traffic Measurement and Analysis Conference (TMA) %C Vienna, Austria %D 2018 %T A Wrapper for Automatic Measurements with YouTube's Native Android App %X YouTube is one of the most popular and demanding services in the Internet today. Thereby, a large portion of this traffic is generated by YouTube's mobile app. While past studies have shown how to monitor browser-based streaming on desktop PCs (e.g., YoMo) or mobile devices (e.g., YoMoApp), streaming in the native app has not been monitored yet. This paper presents an automated framework for monitoring the streaming in YouTube's native app for Android. The concept is based on a wrapper application and the Android Debug Bridge (adb), and can be also extended to automatic measurements with other apps. For YouTube, it allows to collect application-layer streaming data, such as current playtime, buffered playtime, video encoding, and quality switches. These data can be complemented with network measurements on the mobile access link to obtain a holistic view on mobile YouTube streaming on Android devices. In addition to describing the software design and testbed setup, this paper discusses an experimental measurement. This study analyzes the streaming in the native YouTube app and compares it to the streaming from the mobile YouTube website via YoMoApp.
4.
Loh, F., Karagkioules, T., Seufert, M., Zeidler, B., Tsilimantos, D., Tran-Gia, P., Valentin, S., Wamser, F.: A Wrapper for Automated Measurements with YouTube’s Native App, (2018).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
This demo introduces a wrapper used for automated measurements of mobile video streaming in the Android YouTube app. The difference to traditional measurement techniques is that the measurement is done with the native YouTube app as it is provided in the Google Play Store. In addition to bandwidth or packet loss detection, the QoE of the video stream can be measured and quantified. For this, the amount of quality changes, the current playtime, the buffer level, and statistics like video and audio format are captured. Thus, detailed relationships between network parameters and streaming behavior based on many factors can be detected within the native app available in the Play Store.
@misc{info3-demo-2018-1, abstract = {This demo introduces a wrapper used for automated measurements of mobile video streaming in the Android YouTube app. The difference to traditional measurement techniques is that the measurement is done with the native YouTube app as it is provided in the Google Play Store. In addition to bandwidth or packet loss detection, the QoE of the video stream can be measured and quantified. For this, the amount of quality changes, the current playtime, the buffer level, and statistics like video and audio format are captured. Thus, detailed relationships between network parameters and streaming behavior based on many factors can be detected within the native app available in the Play Store.}, address = {Taipei, Taiwan}, author = {Loh, Frank and Karagkioules, Theodoros and Seufert, Michael and Zeidler, Bernd and Tsilimantos, Dimitrios and Tran-Gia, Phuoc and Valentin, Stefan and Wamser, Florian}, booktitle = {IEEE/IFIP NOMS 2018}, keywords = {youtube myown selected-findings-and-results-youtube sys:hidden:software can}, month = 4, title = {A Wrapper for Automated Measurements with YouTube’s Native App}, year = 2018 }
%0 Generic %1 info3-demo-2018-1 %A Loh, Frank %A Karagkioules, Theodoros %A Seufert, Michael %A Zeidler, Bernd %A Tsilimantos, Dimitrios %A Tran-Gia, Phuoc %A Valentin, Stefan %A Wamser, Florian %B IEEE/IFIP NOMS 2018 %C Taipei, Taiwan %D 2018 %T A Wrapper for Automated Measurements with YouTube’s Native App %X This demo introduces a wrapper used for automated measurements of mobile video streaming in the Android YouTube app. The difference to traditional measurement techniques is that the measurement is done with the native YouTube app as it is provided in the Google Play Store. In addition to bandwidth or packet loss detection, the QoE of the video stream can be measured and quantified. For this, the amount of quality changes, the current playtime, the buffer level, and statistics like video and audio format are captured. Thus, detailed relationships between network parameters and streaming behavior based on many factors can be detected within the native app available in the Play Store.

2017

1.
Loh, F., Burger, V., Wamser, F., Tran-Gia, P., Schembra, G., Rametta, C.: Performance Evaluation of Video Streaming Service Chains in Softwarized 5G Networks with Task Graph Reduction. 29th International Teletraffic Congress (ITC) (2017).
[ BibTeX ] [ EndNote ] [ Download ]
 
@inproceedings{info3-inproceedings-2017-18, author = {Loh, Frank and Burger, Valentin and Wamser, Florian and Tran-Gia, Phuoc and Schembra, Giovanni and Rametta, Corrado}, booktitle = {29th International Teletraffic Congress (ITC)}, keywords = {myown input sys:hidden:inproceedings can}, month = 9, title = {Performance Evaluation of Video Streaming Service Chains in Softwarized 5G Networks with Task Graph Reduction}, year = 2017 }
%0 Conference Paper %1 info3-inproceedings-2017-18 %A Loh, Frank %A Burger, Valentin %A Wamser, Florian %A Tran-Gia, Phuoc %A Schembra, Giovanni %A Rametta, Corrado %B 29th International Teletraffic Congress (ITC) %D 2017 %T Performance Evaluation of Video Streaming Service Chains in Softwarized 5G Networks with Task Graph Reduction
2.
Loh, F.: Performance Evaluation of Service Chains in Cloud Networks Using Discrete-Time Analysis, (2017).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{info3-master-2017-6, author = {Loh, Frank}, keywords = {myown input sys:hidden:master sys:hidden:supervisor:valentin.burger sys:hidden:completed sys:hidden:supervisor:phuoc.tran-gia can}, school = {University of Würzburg}, title = {Performance Evaluation of Service Chains in Cloud Networks Using Discrete-Time Analysis}, year = 2017 }
%0 Thesis %1 info3-master-2017-6 %A Loh, Frank %D 2017 %T Performance Evaluation of Service Chains in Cloud Networks Using Discrete-Time Analysis
3.
Wamser, F., Loh, F., Bruschi, R., Lago, P., Seufert, M., Tran-Gia, P.: Dynamic Cloud Service Placement for Live Video Streaming with a Remote-Controlled Drone, (2017).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
The continuous growth of Internet content, applications and services has led to ever more demanding requirements in the networks. To meet this, there have been significant developments in the migration of applications and services towards the cloud. The cloud not only provides applications, but also offers dynamic adaptability, scalability, and optimization with respect to the location of the application by migrating storage and application containers. In particular, cloud services can typically be placed and orchestrated at a variety of locations in the cloud. This demonstration shows a dynamic placement of a cloud service with video streaming and real-time control commands for a remote-controlled drone. We control a drone with the help of a cloud service which, in addition to the control commands, also returns a camera image to the operator. The requirements of this cloud application are groundbreaking. The camera requires high data rates from the drone to the cloud and from the cloud to the operator, so that the image can be transmitted in acceptable quality. For the control of the drone, a short delay must also be given in the direction of the drone so that the control can be carried out in real time.
@misc{info3-demo-2017-1, abstract = {The continuous growth of Internet content, applications and services has led to ever more demanding requirements in the networks. To meet this, there have been significant developments in the migration of applications and services towards the cloud. The cloud not only provides applications, but also offers dynamic adaptability, scalability, and optimization with respect to the location of the application by migrating storage and application containers. In particular, cloud services can typically be placed and orchestrated at a variety of locations in the cloud. This demonstration shows a dynamic placement of a cloud service with video streaming and real-time control commands for a remote-controlled drone. We control a drone with the help of a cloud service which, in addition to the control commands, also returns a camera image to the operator. The requirements of this cloud application are groundbreaking. The camera requires high data rates from the drone to the cloud and from the cloud to the operator, so that the image can be transmitted in acceptable quality. For the control of the drone, a short delay must also be given in the direction of the drone so that the control can be carried out in real time.}, address = {Best Demo Award, Lisbon, Portugal}, author = {Wamser, Florian and Loh, Frank and Bruschi, Roberto and Lago, Paolo and Seufert, Michael and Tran-Gia, Phuoc}, booktitle = {15th IFIP/IEEE International Symposium on Integrated Network Management (IM)}, keywords = {myown input sys:hidden:software can}, month = 5, title = {Dynamic Cloud Service Placement for Live Video Streaming with a Remote-Controlled Drone}, year = 2017 }
%0 Generic %1 info3-demo-2017-1 %A Wamser, Florian %A Loh, Frank %A Bruschi, Roberto %A Lago, Paolo %A Seufert, Michael %A Tran-Gia, Phuoc %B 15th IFIP/IEEE International Symposium on Integrated Network Management (IM) %C Best Demo Award, Lisbon, Portugal %D 2017 %T Dynamic Cloud Service Placement for Live Video Streaming with a Remote-Controlled Drone %X The continuous growth of Internet content, applications and services has led to ever more demanding requirements in the networks. To meet this, there have been significant developments in the migration of applications and services towards the cloud. The cloud not only provides applications, but also offers dynamic adaptability, scalability, and optimization with respect to the location of the application by migrating storage and application containers. In particular, cloud services can typically be placed and orchestrated at a variety of locations in the cloud. This demonstration shows a dynamic placement of a cloud service with video streaming and real-time control commands for a remote-controlled drone. We control a drone with the help of a cloud service which, in addition to the control commands, also returns a camera image to the operator. The requirements of this cloud application are groundbreaking. The camera requires high data rates from the drone to the cloud and from the cloud to the operator, so that the image can be transmitted in acceptable quality. For the control of the drone, a short delay must also be given in the direction of the drone so that the control can be carried out in real time.

2016

1.
Seufert, M., Burger, V., Lorey, K., Seith, A., Loh, F., Tran-Gia, P.: Assessment of Subjective Influence and Trust with an Online Social Network Game. Computers in Human Behavior. 64, 233–246 (2016).
[ Abstract ] [ BibTeX ] [ EndNote ] [ Download ]
 
The deduction of influence and trust between two individuals only from objective data in online social networks (OSNs) is a rather vague approach. Subjective assessments via surveys produce better results, but are harder to conduct considering the vast amount of friendships of OSN users. This work presents a framework for personalized surveys on relationships in OSNs, which follows a gamification approach. A Facebook game was developed, which was used to subjectively assess social influence and interpersonal trust based on models from psychology. The results show that it is possible to obtain subjective opinions and (limited) objective data about relationships with an OSN game. Also an implicit assessment of influence and trust with subcategory questions is feasible in this case.
@article{info3-article-2016-7, abstract = {The deduction of influence and trust between two individuals only from objective data in online social networks (OSNs) is a rather vague approach. Subjective assessments via surveys produce better results, but are harder to conduct considering the vast amount of friendships of OSN users. This work presents a framework for personalized surveys on relationships in OSNs, which follows a gamification approach. A Facebook game was developed, which was used to subjectively assess social influence and interpersonal trust based on models from psychology. The results show that it is possible to obtain subjective opinions and (limited) objective data about relationships with an OSN game. Also an implicit assessment of influence and trust with subcategory questions is feasible in this case.}, author = {Seufert, Michael and Burger, Valentin and Lorey, Karl and Seith, Alexander and Loh, Frank and Tran-Gia, Phuoc}, journal = {Computers in Human Behavior}, keywords = {myown sys:hidden:article can}, month = 11, pages = {233-246}, title = {Assessment of Subjective Influence and Trust with an Online Social Network Game}, volume = 64, year = 2016 }
%0 Journal Article %1 info3-article-2016-7 %A Seufert, Michael %A Burger, Valentin %A Lorey, Karl %A Seith, Alexander %A Loh, Frank %A Tran-Gia, Phuoc %D 2016 %J Computers in Human Behavior %P 233-246 %T Assessment of Subjective Influence and Trust with an Online Social Network Game %V 64 %X The deduction of influence and trust between two individuals only from objective data in online social networks (OSNs) is a rather vague approach. Subjective assessments via surveys produce better results, but are harder to conduct considering the vast amount of friendships of OSN users. This work presents a framework for personalized surveys on relationships in OSNs, which follows a gamification approach. A Facebook game was developed, which was used to subjectively assess social influence and interpersonal trust based on models from psychology. The results show that it is possible to obtain subjective opinions and (limited) objective data about relationships with an OSN game. Also an implicit assessment of influence and trust with subcategory questions is feasible in this case.

2014

1.
Loh, F.: Determination of Traffic Patterns of the Facebook Mobile App and the WhatsApp Messenger, (2014).
[ BibTeX ] [ EndNote ]
 
@mastersthesis{info3-bachelor2014-32, author = {Loh, Frank}, keywords = {myown sys:hidden:completed sys:hidden:bachelor sys:hidden:supervisor:florian.wamser}, month = 12, school = {University of Wuerzburg}, title = {Determination of Traffic Patterns of the Facebook Mobile App and the WhatsApp Messenger}, year = 2014 }
%0 Thesis %1 info3-bachelor2014-32 %A Loh, Frank %D 2014 %T Determination of Traffic Patterns of the Facebook Mobile App and the WhatsApp Messenger