Publikationen - Lehrstuhl für Informatik III

Buch oder Konferenzband

Antons, J.-N., Egger, S., Hoßfeld, T., Möller, S.: Proceedings of The Fifth Workshop on Perceptual Quality of Systems (PQS). ISCA, TU Berlin, Germany (2016).

[ BibTeX ] [ EndNote ]

@book{info3-book-2016-3, address = {TU Berlin, Germany}, author = {Antons, Jan-Niklas and Egger, Sebastian and Hoßfeld, Tobias and Möller, Sebastian}, keywords = {myown sys:hidden:book}, month = 8, publisher = {ISCA}, title = {Proceedings of The Fifth Workshop on Perceptual Quality of Systems (PQS)}, year = 2016 }

%0 Book %1 info3-book-2016-3 %A Antons, Jan-Niklas %A Egger, Sebastian %A Hoßfeld, Tobias %A Möller, Sebastian %C TU Berlin, Germany %D 2016 %I ISCA %T Proceedings of The Fifth Workshop on Perceptual Quality of Systems (PQS)
Hoßfeld, T., Chan, G., Mark, B.L., Timm-Giel, A.: Proceedings of The 28th International Teletraffic Congress 2016 (ITC 28). ITC, IEEE, ACM, Würzburg, Germany (2016).

[ BibTeX ] [ EndNote ]

@book{info3-book-2016-2, address = {Würzburg, Germany}, author = {Hoßfeld, Tobias and Chan, Gary and Mark, Brian L. and Timm-Giel, Andreas}, keywords = {myown sys:hidden:book}, month = 9, publisher = {ITC, IEEE, ACM}, title = {Proceedings of The 28th International Teletraffic Congress 2016 (ITC 28)}, year = 2016 }

%0 Book %1 info3-book-2016-2 %A Hoßfeld, Tobias %A Chan, Gary %A Mark, Brian L. %A Timm-Giel, Andreas %C Würzburg, Germany %D 2016 %I ITC, IEEE, ACM %T Proceedings of The 28th International Teletraffic Congress 2016 (ITC 28)
Agüero, R., Zinner, T., García-Lozano, M., Wenning, B.-L., Timm-Giel, A.: Mobile Networks and Management. Springer, Santander, Spain (2016).

[ BibTeX ] [ EndNote ]

@book{info3-book-2016-1, address = {Santander, Spain}, author = {Agüero, Ramón and Zinner, Thomas and García-Lozano, Mario and Wenning, Bernd-Ludwig and Timm-Giel, Andreas}, booktitle = {6th International Conference (MONAMI 2014), September 22-26, 2014, Revised Selected Papers}, keywords = {myown sys:hidden:book}, publisher = {Springer}, title = {Mobile Networks and Management}, year = 2016 }

%0 Book %1 info3-book-2016-1 %A Agüero, Ramón %A Zinner, Thomas %A García-Lozano, Mario %A Wenning, Bernd-Ludwig %A Timm-Giel, Andreas %B 6th International Conference (MONAMI 2014), September 22-26, 2014, Revised Selected Papers %C Santander, Spain %D 2016 %I Springer %T Mobile Networks and Management

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Buchkapitel

Liotou, E., Hoßfeld, T., Moldovan, C., Metzger, F., Tsolkas, D., Passas, N.: The Value of Context-Awareness in Bandwidth-Challenging HTTP Adaptive Streaming Scenarios. In: Ganchev, I., van der Mei, R.D., and van den Berg, H. (eds.) Autonomous Control for a Reliable Internet of Services: Methods, Models, Approaches, Techniques, Algorithms, and Tools. Springer International Publishing (2018).

[ Abstract ] [ BibTeX ] [ EndNote ]

Video streaming has become an indispensable technology in people’s lives, while its usage keeps constantly increasing. The variability, instability and unpredictability of network conditions pose one of the biggest challenges to video streaming. In this chapter, we analyze HTTP Adaptive Streaming, a technology that relieves these issues by adapting the video reproduction to the current network conditions. Particularly, we study how context awareness can be combined with the adaptive streaming logic to design a proactive client-based video streaming strategy. Our results show that such a context-aware strategy manages to successfully mitigate stallings in light of network connectivity problems, such as an outage. Moreover, we analyze the performance of this strategy by comparing it to the optimal case, as well as by considering situations where the awareness of the context lacks reliability.

@incollection{info3-incollection-2018-4, abstract = {Video streaming has become an indispensable technology in people’s lives, while its usage keeps constantly increasing. The variability, instability and unpredictability of network conditions pose one of the biggest challenges to video streaming. In this chapter, we analyze HTTP Adaptive Streaming, a technology that relieves these issues by adapting the video reproduction to the current network conditions. Particularly, we study how context awareness can be combined with the adaptive streaming logic to design a proactive client-based video streaming strategy. Our results show that such a context-aware strategy manages to successfully mitigate stallings in light of network connectivity problems, such as an outage. Moreover, we analyze the performance of this strategy by comparing it to the optimal case, as well as by considering situations where the awareness of the context lacks reliability.}, author = {Liotou, Eirini and Hoßfeld, Tobias and Moldovan, Christian and Metzger, Florian and Tsolkas, Dimitris and Passas, Nikos}, booktitle = {Autonomous Control for a Reliable Internet of Services: Methods, Models, Approaches, Techniques, Algorithms, and Tools}, editor = {Ganchev, Ivan and van der Mei, R. D. and van den Berg, Hans}, keywords = {myown sys:hidden:incollection fullstack}, month = 5, publisher = {Springer International Publishing}, title = {The Value of Context-Awareness in Bandwidth-Challenging HTTP Adaptive Streaming Scenarios}, year = 2018 }

%0 Book Section %1 info3-incollection-2018-4 %A Liotou, Eirini %A Hoßfeld, Tobias %A Moldovan, Christian %A Metzger, Florian %A Tsolkas, Dimitris %A Passas, Nikos %B Autonomous Control for a Reliable Internet of Services: Methods, Models, Approaches, Techniques, Algorithms, and Tools %D 2018 %E Ganchev, Ivan %E van der Mei, R. D. %E van den Berg, Hans %I Springer International Publishing %T The Value of Context-Awareness in Bandwidth-Challenging HTTP Adaptive Streaming Scenarios %X Video streaming has become an indispensable technology in people’s lives, while its usage keeps constantly increasing. The variability, instability and unpredictability of network conditions pose one of the biggest challenges to video streaming. In this chapter, we analyze HTTP Adaptive Streaming, a technology that relieves these issues by adapting the video reproduction to the current network conditions. Particularly, we study how context awareness can be combined with the adaptive streaming logic to design a proactive client-based video streaming strategy. Our results show that such a context-aware strategy manages to successfully mitigate stallings in light of network connectivity problems, such as an outage. Moreover, we analyze the performance of this strategy by comparing it to the optimal case, as well as by considering situations where the awareness of the context lacks reliability.
Arvidsson, A., van den Berg, H., Burakowski, W., Grbac, T.G., Ganchev, I., Haddad, Y., Hoßfeld, T., Key, P., Lassila, P., Leitner, P.: State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services. Autonomous Control for a Reliable Internet of Services. Springer (2018).

[ BibTeX ] [ EndNote ]

@incollection{info3-incollection-2018-5, author = {Arvidsson, Ake and van den Berg, Hans and Burakowski, Wojciech and Grbac, Tihana Galinac and Ganchev, Ivan and Haddad, Yoram and Hoßfeld, Tobias and Key, Peter and Lassila, Pasi and Leitner, Philipp}, booktitle = {Autonomous Control for a Reliable Internet of Services.}, keywords = {myown sys:hidden:incollection}, publisher = {Springer}, title = {State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services}, year = 2018 }

%0 Book Section %1 info3-incollection-2018-5 %A Arvidsson, Ake %A van den Berg, Hans %A Burakowski, Wojciech %A Grbac, Tihana Galinac %A Ganchev, Ivan %A Haddad, Yoram %A Hoßfeld, Tobias %A Key, Peter %A Lassila, Pasi %A Leitner, Philipp %B Autonomous Control for a Reliable Internet of Services. %D 2018 %I Springer %T State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services
Archambault, D., Purchase, H.C., Hoßfeld, T.: Evaluation in the Crowd: An Introduction. Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments. Springer, Cham (2017).

[ BibTeX ] [ EndNote ]

@incollection{info3-incollection-2017-5, author = {Archambault, Daniel and Purchase, Helen C and Hoßfeld, Tobias}, booktitle = {Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments}, keywords = {myown sys:hidden:incollection}, publisher = {Springer, Cham}, title = {Evaluation in the Crowd: An Introduction}, year = 2017 }

%0 Book Section %1 info3-incollection-2017-5 %A Archambault, Daniel %A Purchase, Helen C %A Hoßfeld, Tobias %B Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments %D 2017 %I Springer, Cham %T Evaluation in the Crowd: An Introduction

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Artikel in einem Journal

Wassermann, S., Wehner, N., Casas, P.: Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones. ACM SIGMETRICS Performance Evaluation Review. 46, 155-158 (2019).

[ Abstract ] [ BibTeX ] [ EndNote ]

Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.

@article{info3-article-2019-10, abstract = {Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.}, author = {Wassermann, Sarah and Wehner, Nikolas and Casas, Pedro}, journal = {ACM SIGMETRICS Performance Evaluation Review}, keywords = {myown sys:hidden:article}, month = 1, number = 3, pages = {155-158}, title = {Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones}, volume = 46, year = 2019 }

%0 Journal Article %1 info3-article-2019-10 %A Wassermann, Sarah %A Wehner, Nikolas %A Casas, Pedro %D 2019 %J ACM SIGMETRICS Performance Evaluation Review %N 3 %P 155-158 %T Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones %V 46 %X Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.
Hoßfeld, T., Skorin-Kapov, L., Varela, M., Chen, K.-T.: Guest Editorial: Special Issue on “QoE Management for Multimedia Services”. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). (2018).

[ BibTeX ] [ EndNote ]

@article{info3-article-2018-12, author = {Hoßfeld, Tobias and Skorin-Kapov, Lea and Varela, Martin and Chen, Kuan-Ta}, journal = {ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)}, keywords = {myown sys:hidden:article}, title = {Guest Editorial: Special Issue on “QoE Management for Multimedia Services”}, year = 2018 }

%0 Journal Article %1 info3-article-2018-12 %A Hoßfeld, Tobias %A Skorin-Kapov, Lea %A Varela, Martin %A Chen, Kuan-Ta %D 2018 %J ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) %T Guest Editorial: Special Issue on “QoE Management for Multimedia Services”
Hoßfeld, T., Tran-Gia, P., Vukovic, M.: Special issue on crowdsourcing. Computer Networks: The International Journal of Computer and Telecommunications Networking. (2015).

[ BibTeX ] [ EndNote ]

@article{info3-article-2015-10, author = {Hoßfeld, Tobias and Tran-Gia, Phuoc and Vukovic, Maja}, journal = {Computer Networks: The International Journal of Computer and Telecommunications Networking}, keywords = {myown sys:hidden:article}, title = {Special issue on crowdsourcing}, year = 2015 }

%0 Journal Article %1 info3-article-2015-10 %A Hoßfeld, Tobias %A Tran-Gia, Phuoc %A Vukovic, Maja %D 2015 %J Computer Networks: The International Journal of Computer and Telecommunications Networking %T Special issue on crowdsourcing

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Artikel in Konferenzband

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 ]

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 = {myown sys:hidden:inproceedings huawei 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.
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 ]

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 = {myown sys:hidden:inproceedings huawei 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.
Wassermann, S., Wehner, N., Casas, P.: Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones. Workshop on AI in Networks (WAIN) (2018).

[ Abstract ] [ BibTeX ] [ EndNote ]

Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and user-behavior-related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.

@inproceedings{info3-inproceedings-2018-51, abstract = {Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and user-behavior-related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.}, author = {Wassermann, Sarah and Wehner, Nikolas and Casas, Pedro}, booktitle = {Workshop on AI in Networks (WAIN)}, keywords = {myown sys:hidden:inproceedings}, month = 12, title = {Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones}, year = 2018 }

%0 Conference Paper %1 info3-inproceedings-2018-51 %A Wassermann, Sarah %A Wehner, Nikolas %A Casas, Pedro %B Workshop on AI in Networks (WAIN) %D 2018 %T Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones %X Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and user-behavior-related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.

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Reports

Gebert, S., Zinner, T., Lange, S., Schwartz, C., Tran-Gia, P.: Discrete-Time Analysis: Deriving the Distribution of the Number of Events in an Arbitrarily Distributed Interval. University of Wuerzburg (2016).

[ BibTeX ] [ EndNote ] [ Download ]

@techreport{info3-techreport-2016-1, author = {Gebert, Steffen and Zinner, Thomas and Lange, Stanislav and Schwartz, Christian and Tran-Gia, Phuoc}, institution = {University of Wuerzburg}, keywords = {myown sys:hidden:techreport}, month = 6, number = 498, title = {Discrete-Time Analysis: Deriving the Distribution of the Number of Events in an Arbitrarily Distributed Interval}, year = 2016 }

%0 Report %1 info3-techreport-2016-1 %A Gebert, Steffen %A Zinner, Thomas %A Lange, Stanislav %A Schwartz, Christian %A Tran-Gia, Phuoc %D 2016 %N 498 %T Discrete-Time Analysis: Deriving the Distribution of the Number of Events in an Arbitrarily Distributed Interval
Seufert, M., Hoßfeld, T., Sperb Machado, G., Bocek, T., Biancani, M., Cruschelli, P., Lapacz, R., Darzanos, G., Papafili, I., Wajda, K.: A Survey of Cloud Services and Potential Applications of Social Awareness. University of Wuerzburg (2015).

[ BibTeX ] [ EndNote ]

@techreport{info3-techreport-2015-3, author = {Seufert, Michael and Hoßfeld, Tobias and Sperb Machado, Guilherme and Bocek, Thomas and Biancani, Matteo and Cruschelli, Paolo and Lapacz, Roman and Darzanos, George and Papafili, Ioanna and Wajda, Krzysztof}, institution = {University of Wuerzburg}, keywords = {myown sys:hidden:techreport smartenit can}, month = 11, number = 496, title = {A Survey of Cloud Services and Potential Applications of Social Awareness}, year = 2015 }

%0 Report %1 info3-techreport-2015-3 %A Seufert, Michael %A Hoßfeld, Tobias %A Sperb Machado, Guilherme %A Bocek, Thomas %A Biancani, Matteo %A Cruschelli, Paolo %A Lapacz, Roman %A Darzanos, George %A Papafili, Ioanna %A Wajda, Krzysztof %D 2015 %N 496 %T A Survey of Cloud Services and Potential Applications of Social Awareness
Schwartz, C., Hoßfeld, T., Lehrieder, F., Tran-Gia, P.: Performance Analysis of the Trade-off Between Signalling Load and Power Consumption for Popular Smartphone Apps in 3G Networks. University of Wuerzburg (2013).

[ BibTeX ] [ EndNote ] [ Download ]

@techreport{info3-techreport-2013-260, author = {Schwartz, Christian and Hoßfeld, Tobias and Lehrieder, Frank and Tran-Gia, Phuoc}, institution = {University of Wuerzburg}, keywords = {myown sys:hidden:techreport}, month = 1, number = 485, title = {Performance Analysis of the Trade-off Between Signalling Load and Power Consumption for Popular Smartphone Apps in 3G Networks}, year = 2013 }

%0 Report %1 info3-techreport-2013-260 %A Schwartz, Christian %A Hoßfeld, Tobias %A Lehrieder, Frank %A Tran-Gia, Phuoc %D 2013 %N 485 %T Performance Analysis of the Trade-off Between Signalling Load and Power Consumption for Popular Smartphone Apps in 3G Networks

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Dissertationen

Metter, C.: Resilience, Availability, and Serviceability Evaluation in Software-defined Networks, (2019).

[ BibTeX ] [ EndNote ]

@phdthesis{info3-dissertation-2019-1, author = {Metter, Christopher}, keywords = {myown sys:hidden:dissertation sys:hidden:supervisor:phuoc.tran-gia}, month = 1, school = {Universitiy of Würzburg}, title = {Resilience, Availability, and Serviceability Evaluation in Software-defined Networks}, year = 2019 }

%0 Thesis %1 info3-dissertation-2019-1 %A Metter, Christopher %D 2019 %T Resilience, Availability, and Serviceability Evaluation in Software-defined Networks
Lange, S.: Optimization of Controller Placement and Information Flow in Softwarized Networks, (2018).

[ BibTeX ] [ EndNote ]

@phdthesis{info3-dissertation-2018-3, author = {Lange, Stanislav}, keywords = {myown sys:hidden:dissertation sys:hidden:supervisor:phuoc.tran-gia}, month = 12, school = {Universitiy of Würzburg}, title = {Optimization of Controller Placement and Information Flow in Softwarized Networks}, year = 2018 }

%0 Thesis %1 info3-dissertation-2018-3 %A Lange, Stanislav %D 2018 %T Optimization of Controller Placement and Information Flow in Softwarized Networks
Tran-Gia, P.: Zeitdiskrete Analyse verkehrstheoretischer Modelle in Recher- und Kommunikationssystemen, (1988).

[ BibTeX ] [ EndNote ]

@phdthesis{info3-dissertation-1988-1, author = {Tran-Gia, Phuoc}, keywords = {myown sys:hidden:dissertation sys:hidden:supervisor:paul.kühn}, school = {Universität Stuttgart}, title = {Zeitdiskrete Analyse verkehrstheoretischer Modelle in Recher- und Kommunikationssystemen}, year = 1988 }

%0 Thesis %1 info3-dissertation-1988-1 %A Tran-Gia, Phuoc %D 1988 %T Zeitdiskrete Analyse verkehrstheoretischer Modelle in Recher- und Kommunikationssystemen

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Buch oder Konferenzband

Buchkapitel

Artikel in einem Journal

Artikel in Konferenzband

Reports

Dissertationen

Hinweis zum Datenschutz

Hinweis zum Datenschutz

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