1.
Seufert, A., Schweifler, R., Poignée, F., Seufert, M., Hoßfeld, T.: Waiting along the Path: How Browsing Delays Impact the QoE of Music Streaming Applications. 14th International Conference on Quality of Multimedia Experience (QoMEX) (2022).
Streaming has become the dominant source of media consumption, which not only applies to the widely researched field of video streaming, but also to music streaming. Here, previous studies so far have only researched the impact of streaming aspects, such as stalling events or initial loading times, on the QoE of music streaming. However, when using a music streaming application, users are already facing waiting times along the click path before they can start the actual streaming. These waiting times are caused by browsing delays, e.g., during searching for songs or scrolling through playlists, and can potentially deteriorate the QoE of the music streaming application. In this work, we conduct an online QoE study to quantify the impact of these browsing delays with the support of an emulated mobile music streaming web app. We found that browsing delays have no impact on the music streaming QoE, which shows that users are able to clearly distinguish between the two main functionalities of such apps, namely, browsing and streaming. However, browsing delays significantly reduce the QoE of the entire music streaming application, to a similar extent as if QoE degradations happen during the actual streaming. This shows that both browsing and streaming are equally important and have to be considered when designing music streaming applications.
2.
Casas, P., Wassermann, S., Seufert, M., Wehner, N., Dinica, O., Hoßfeld, T.: X-Ray Goggles for the ISP: Improving in-Network Web and App QoE Monitoring with Deep Learning. Network Traffic Measurement and Analysis Conference (TMA) (2022).
The wide adoption of end-to-end encryption is drastically limiting the visibility Internet Service Providers (ISPs) have on the performance of the services consumed by their customers. In times of strong competition, where customer experience plays a key role in churn management, ISPs require novel solutions enabling network-wide Quality of Experience (QoE) monitoring. To this end, we present DeepQoE, a deep-learning based approach to infer the QoE of web services and mobile applications from the ISP perspective, relying exclusively on the analysis of encrypted network traffic. Using raw features derived from the encrypted stream of bytes as input to deep Convolutional Neural Networks (CNNs), DeepQoE infers the Speed Index of web browsing sessions and general mobile apps with unprecedented accuracy, improving the state of the art by more than 25%, and reducing the QoE inference error in terms of mean opinion scores by nearly 40%. DeepQoE implements a web fingerprinting solution to identify individual web browsing sessions within concurrent web pages traffic, enabling highly detailed, per web page QoE inference in practical deployments. Extensive evaluations over a large and heterogeneous dataset composed of web and app measurements, using different device types and for top-popular websites and apps, confirm the out-performance of DeepQoE over previously used shallow-learning models, as well as the deep-model generalization to different devices, web pages, apps, and network setups. DeepQoE is the first deployable system providing such a deep, highly-detailed QoE for individual web browsing and mobile apps over encrypted traffic, using deep learning models on heterogeneous measurements.
3.
Casas, P., Wassermann, S., Wehner, N., Seufert, M., Hoßfeld, T.: Not all Web Pages are Born the Same. Content Tailored Learning for Web QoE Inference. IEEE International Symposium on Measurements & Networking (M&N 2022) (2022).
Web Quality of Experience (QoE) monitoring is a critical task for Internet Service Providers (ISPs), especially due to the key role played by customer experience in churn management. Previously, we have tackled the problem of Web QoE inference from the ISP perspective, relying on passive measurement of encrypted network traffic and machine learning models. In this paper, we exploit the broad heterogeneity of contents embedded in web pages to improve the state of the art performance in Web QoE inference, relying on web-content learning model tailoring. By analyzing the top-500 most popular web pages of the Internet through unsupervised learning, we discover different web page content classes which realize significantly different Web QoE inference performance. We train supervised learning inference models separately for each of these classes, using the well-known Speed Index (SI) metric as proxy to Web QoE. Empirical evaluations on a large corpus of Web QoE measurements for top popular websites demonstrate that our combined content-tailored approach improves the inference performance of the SI by almost 30% with respect to previous single-model approaches, reducing the QoE inference error in terms of mean opinion scores by more than 40%.
4.
Casas, P., Seufert, M., Wassermann, S., Gardlo, B., Wehner, N., Schatz, R.: DeepCrypt - Deep Learning for QoE Monitoring and Fingerprinting of User Actions in Adaptive Video Streaming. IEEE Conference on Network Softwarization (IEEE NetSoft 2022) (2022).
We introduce DeepCrypt, a deep-learning based approach to analyze YouTube video streaming Quality of Experience (QoE) from the Internet Service Provider (ISP) perspective, relying exclusively on the analysis of encrypted network traffic. Using raw features derived on-line from the encrypted stream of bytes, DeepCrypt can infer six different video QoE indicators capturing the user-perceived performance of the service, including the initial playback delay, the number and frequency of rebuffering events, the video playback quality, the video encoding bitrate, and the number of quality changes. DeepCrypt offers deep visibility into the behavior of the end-user, enabling the fingerprinting and detection of different user actions on the video player, such as video pauses and playback scrubbing (forward, backward, out-of-buffer), offering a complete visibility on the video streaming process from in-network traffic measurements. Extensive evaluations over a large and heterogeneous dataset composed of mobile and fixed-line measurements, using the YouTube HTML5 player, the native YouTube mobile app, as well as a generic HTML5 video player built on top of open source libraries, and considering measurements collected at different ISPs, confirm the out-performance of DeepCrypt over previously used shallow-learning models, as well as the deep-model generalization to different video players and network setups. To the best of our knowledge, this is the first paper tackling such a combined QoE and user-actions approach for video streaming applications, using deep learning models over heterogeneous measurements.
5.
Dietz, K., Gray, N., Seufert, M., Hoßfeld, T.: ML-based Performance Prediction of SDN using Simulated Data from Real and Synthetic Networks. 33th IEEE/IFIP Network Operations and Management Symposium (NOMS). , Budapest, Hungary (2022).
6.
Wehner, N., Ring, M., Schüler, J., Hotho, A., Hoßfeld, T., Seufert, M.: On Learning Hierarchical Embeddings from Encrypted Network Traffic. 33th IEEE/IFIP Network Operations and Management Symposium (NOMS). , Budapest, Hungary (2022).
This work presents a novel concept for learning embeddings from encrypted network traffic. In contrast to existing approaches, we evaluate the feasibility of hierarchical embeddings by iteratively aggregating packet embeddings to flow embeddings, and flow embeddings to trace embeddings. The hierarchical embedding concept was designed to especially consider complex dependencies of Internet traffic on different time scales. We describe this novel embedding concept for the domain of network traffic in full detail, and evaluate its performance for the downstream task of website fingerprinting, i.e., identifying websites from encrypted traffic, which is relevant for network management, e.g., as a prerequisite for QoE monitoring or for intrusion detection. Our evaluation reveals that embeddings are a promising solution for website fingerprinting as our model correctly labels up to 99.8% of traces from 500 target websites.
7.
Wehner, N., Amir, M., Seufert, M., Schatz, R., Hoßfeld, T.: A Vital Improvement? Relating Google’s Core Web Vitals to Actual Web QoE. 14th International Conference on Quality of Multimedia Experience (QoMEX) (2022).
Providing sophisticated Web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in Web technologies (HTML5, responsive design, etc.), traditional Web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google's Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of Web QoE. To this end, we first perform objective measurements in the Web using Google's Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. In these studies, we use CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals. Our results suggest that the Core Web Vitals have less predictive value for Web QoE than expected and that page loading times remain the main influence factor in this context.
8.
Hoßfeld, T., Seufert, M., Naderi, B.: On Inter-Rater Reliability for Crowdsourced QoE. 13th International Conference on Quality of Multimedia Experience (QoMEX). , Montreal, Canada (Virtual Conference) (2021).
9.
Grigorjew, A., Seufert, M., Wehner, N., Hofmann, J., Hoßfeld, T.: ML-Assisted Latency Assignments in Time-Sensitive Networking. IFIP/IEEE International Symposium on Integrated Network Management (IM 2021). , Bordeaux, France (Virtual Conference) (2021).
Recent developments in industrial automation and invehicle communication have raised the requirements of real-time networking. Bus systems that were traditionally deployed in these fields cannot provide sufficient bandwidth and are now shifting towards Ethernet for their real-time communication needs. In this field, standardization efforts from the IEEE and the IETF have developed new data plane mechanisms such as shapers and schedulers, as well as control plane mechanisms such as reservation protocols to support their new requirements. However, their implementation and their optimal configuration remain an important factor for their efficiency. This work presents a machine learning framework that takes on the configuration task. Four different models are trained for the configuration of per-hop latency guarantees in a distributed resource reservation process and compared with respect to their real-time traffic capacity. The evaluation shows that all models provide good configurations for the provided scenarios, but more importantly, they represent a first step for a semi-automated configuration of parameters in Time-Sensitive Networking.
10.
Wehner, N., Seufert, M., Wieser, V., Casas, P., Capdehourat, G.: Quality that Matters: QoE Monitoring in Education Service Provider (ESP) Networks. IFIP/IEEE International Symposium on Integrated Network Management (IM 2021). , Bordeaux, France (Virtual Conference) (2021).
Education Service Providers (ESPs) play a crucial role in the digitization of education as they equip students and teachers with reliable devices and provide high quality Internet access at schools. This paper investigates four months worth of continuous measurements conducted by an ESP using a largescale, passive, in-device Quality of Experience (QoE) monitoring system deployed into a nationwide network of education-purpose devices. These measurements cover more than 800 education centers and about 4000 devices, used both in schools and at home. Using this rich dataset, we present an exhaustive characterization of the browsing behavior, and a quantification of the web and video QoE in this educational context. Web QoE results showed a better performance for school Wi-Fi networks compared to home connections, suggesting that several issues may arise for ESPs due to the increasing relevance of home-schooling caused by the COVID-19 pandemic.
11.
Gray, N., Dietz, K., Seufert, M., Hoßfeld, T.: High Performance Network Metadata Extraction Using P4 for ML-Based Intrusion Detection Systems. 22nd International Conference on High Performance Switching and Routing (HPSR). , Paris, France (Virtual Conference) (2021).
12.
Orsolic, I., Seufert, M.: On Machine Learning based Video QoE Estimation Across Different Networks. 16th International Conference on Telecommunications (ConTEL), Best Paper Award. , Zagreb, Croatia (Virtual Conference) (2021).
13.
Wehner, N., Seufert, M., Schüler, J., Casas, P., Hoßfeld, T.: How are your Apps Doing? QoE Inference and Analysis in Mobile Devices. 17th International Conference on Network and Service Management (CNSM). , Izmir, Turkey (Virtual Conference) (2021).
Web browsing has become the most important application of the Internet for the end user. When it comes to mobile devices, web services are mainly accessed through apps. This paper tackles the problem of Web Quality of Experience (QoE) in mobile devices, with a specific focus on apps QoE monitoring and analysis, using in-network (encrypted) traffic measurements. Measuring apps QoE is complex, not only from an instrumentation point of view, but also from the heterogeneity of user interactions which might realize substantially different user experience. To this end, we conduct a feasibility study on four specific and popular Android apps and their corresponding web services. Our test automation framework emulates and measures different user interactions commonly executed during an app session, including the app startup, clicking, scrolling, and searching. The resulting traffic is characterized on different dimensions, and machine learning models are trained to identify web services, apps, and user interactions, and to infer their QoE. The proposed models can correctly identify the specific web service and app in 86% of the cases and accurately estimate the associated QoE with small errors. Our preliminary study represents a first step towards an in-network, web QoE monitoring solution for mobile-device apps.
14.
Dietz, K., Mühlhauser, M., Seufert, M., Gray, N., Hoßfeld, T., Herrmann, D.: Browser Fingerprinting: How to Protect Machine Learning Models and Data with Differential Privacy?. 1st International Workshop on Machine Learning in Networking (MaLeNe). , Lübeck, Germany (Virtual Conference) (2021).
15.
Casas, P., Wassermann, S., Wehner, N., Seufert, M., Schüler, J., Hoßfeld, T.: Mobile Web and App QoE Monitoring for ISPs - from Encrypted Traffic to Speed Index through Machine Learning. 13th Wireless and Mobile Networking Conference (WMNC), Best Paper Award. , Montreal, Canada (Virtual Conference) (2021).
16.
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).
17.
Wehner, N., Seufert, M., Schüler, J., Wassermann, S., Casas, P., Hoßfeld, T.: Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling. 2nd Workshop on AI in Networks and Distributed Systems (WAIN). , Milan, Italy (2020).
This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI -- an efficient approximation to SI, with machine-learning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.
18.
Seufert, M., Wehner, N., Wieser, V., Casas, P., Capdehourat, G.: Mind the (QoE) Gap: On the Incompatibility of Web and Video QoE Models in the Wild. 16th International Conference on Network and Service Management (CNSM). , Izmir, Turkey (2020).
Education Service Providers (ESPs) have a paramount role in the digitization of education, providing reliable devices for students and teachers and high quality Internet access at schools. In this paper, a large-scale, passive, in-device Quality of Experience (QoE) monitoring system is presented, which was deployed into a nationwide network of education-purpose devices. Four months' worth of continuous measurements were conducted by an ESP, covering more than 800 education centers and about 4000 devices, used both in schools and at home. When analyzing the QoE of web sessions in school networks, we identify a fundamental issue with the compatibility of web browsing and video QoE models, which inhibits the successful application of QoE-aware network management for multiple services.
19.
Wassermann, S., Casas, P., Ben Houidi, Z., Huet, A., Seufert, M., Wehner, N., Schüler, J., Cai, S.-M., Shi, H., Xu, J., Hoßfeld, T., Rossi, D.: Are you on Mobile or Desktop? On the Impact of End-User Device on Web QoE Inference from Encrypted Traffic. 16th International Conference on Network and Service Management (CNSM). , Izmir, Turkey (2020).
Web browsing is one of the key applications of the Internet, if not the most important one. We address the problem of Web Quality of Experience (QoE) monitoring from the ISP perspective, relying on in-network, passive measurements. As a proxy to Web QoE, we focus on the analysis of the well-known SpeedIndex (SI) metric. Given the lack of app-level data visibility introduced by the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI and the QoE level of individual web page loading sessions, using as input only packet- and flow-level data. Our study targets the impact of different end-user device types (e.g., smartphone, desktop, tablet) on the performance of such models. Empirical evaluations on a large, multi-device, heterogeneous corpus of Web QoE measurements for top popular websites demonstrate that the proposed solution can infer the SI as well as estimate QoE ranges with high accuracy, using either packet-level or flow-level measurements. In addition, we show that the device type adds a strong bias in the feasibility of these Web QoE models, putting into question the applicability of previously conceived approaches on single-device measurements. To improve the state of affairs, we conceive cross-device generalizable models operating at both packet and flow levels, offering a feasible solution for Web QoE monitoring in operational, multi-device networks. To the best of our knowledge, this is the first study tackling the analysis of Web QoE from encrypted network traffic in multi-device scenarios.
20.
Wehner, N., Seufert, M., Egger-Lampl, S., Gardlo, B., Casas, P., Schatz, R.: Scoring High: Analysis and Prediction of Viewer Behavior and Engagement in the Context of 2018 FIFA WC Live Streaming. Proceedings of the 28th ACM International Conference on Multimedia (MM). , Seattle, WA, USA (2020).
21.
Wehner, N., Mertinat, N., Seufert, M., Hoßfeld, T.: Studying the Impact of the Content Selection Method on the Video QoE on Mobile Devices. 12th International Conference on Quality of Multimedia Experience (QoMEX). , Athlone, Ireland (2020).
When conducting video QoE studies, participants are usually asked to rate the QoE of prepared test videos. However, participants are given no choice to select content, which they like or in which they are interested. This may cause annoyance or frustration when conducting the QoE study, which eventually might affect the QoE results of the study. The consequent question is whether the content liking has a direct impact on the submitted ratings by the participants and whether the freedom of choosing the video content in QoE studies results in better ratings. To investigate this research question, MCA, an existing framework for crowdsourced video testing, is extended and used in a pilot field study. MCA runs on mobile devices and allows users to watch the tested conditions of a QoE study within video content of their interest. The results of a QoE study with individual and dynamic content selection are compared to a QoE study with pre-selected contents. Moreover, this work includes a comparison to a previous QoE study for validation. As the previous study was conducted on desktop PCs, the MCA study further allows to identify differences in the stalling perception between studies on desktop PCs and mobile devices.
22.
Seufert, M., Kargl, J., Schauer, J., Nüchter, A., Hoßfeld, T.: Different Points of View: Impact of 3D Point Cloud Reduction on QoE of Rendered Images. 12th International Conference on Quality of Multimedia Experience (QoMEX). , Athlone, Ireland (2020).
Modern photogrammetric methods as well as laser measurement systems make it easy to collect large 3D point clouds that sample objects or environments. As the recorded point clouds can be used to render computer-generated images and models, they are of particular interest in the domains of geographical and architectural engineering, as well for computer graphics (e.g., games or virtual reality). However, point clouds have a huge storage demand, thus, point clouds shall be reduced by removing some of the points. This will inevitably also reduce the Quality of Experience (QoE) of media, which is rendered from the reduced point clouds. In this work, the impact of two different reduction methods on the QoE of rendered images is investigated from two point of views, i.e., based on ratings from both naive crowdworkers as well as point cloud experts.
23.
Borchert, K., Seufert, M., Hildebrand, K., Hoßfeld, T.: QoE Assessment of Enterprise Applications based on Self-motivated Ratings. 12th International Conference on Quality of Multimedia Experience (QoMEX). , Athlone, Ireland (2020).
In most companies, enterprise applications, such as office products or databases, are heavily used by employees during work hours. Impairments and performance issues not only slow down business processes, but might also increase the frustration of the workforce. While Quality of Experience (QoE) has been widely studied for personal multimedia applications, such as video streaming, its application to the business usage domain is still in its infancy. Due to several reasons, e.g., the high complexity of IT infrastructure, classical QoE studies can hardly be transferred to business applications. These studies are often independent from the context of usage and actively poll ratings from their participants. This work contrasts the commonly used "pull" method for collecting user ratings with a self-motivated "push" approach. This approach is inspired by complaint systems, in which users can directly report problems with a technical system as soon as they notice them. Therefore, performance assessments of a business application from employees of a cooperating company are collected with both rating systems during a time span of 1.5 years. Besides the analysis of the interaction of users with the "push" system, differences between the two methods are discussed. Further, QoE models for the monitored business application are derived based on the self-motivated "push" ratings.
24.
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).
25.
Seufert, M., Casas, P., Wehner, N., Li, G., Kuang, L.: Features that Matter: Feature Selection for On-line Stalling Prediction in Encrypted Video Streaming. 2nd International Workshop on Network Intelligence (NI). , Paris, France (2019).
26.
Seufert, M., Schatz, R., Wehner, N., Casas, P.: QUICker or not? - an Empirical Analysis of QUIC vs TCP for Video Streaming QoE Provisioning. 3rd International Workshop on Quality of Experience Management (QoE-Management). , Paris, France (2019).
The introduction of the QUIC (Quick UDP Internet Connections) transport protocol by Google aimed to improve the Quality of Experience (QoE) with web services compared to the prevailing Transport Control Protocol (TCP). Nowadays, QUIC has become the default protocol to communicate between the Google Chrome browser and Google servers and accounts for an increasing share of the Internet traffic. This work investigates whether the promised QoE benefits of QUIC are indeed noticeable for end users or not. A measurement study was conducted for YouTube video streaming in two mobile and two fixed access networks, in which a defined set of videos was streamed back-to-back with QUIC and TCP in randomized order. QoE factors of video streaming (such as initial delay, the visual quality of the video, and stalling) were compared statistically to find significant differences between the streaming over QUIC and the streaming over TCP. Surprisingly, no evidence for any QoE improvement of QUIC over TCP in the context of YouTube streaming could be found.
27.
Seufert, M., Casas, P., Wehner, N., Li, G., Kuang, L.: Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming Traffic. 3rd International Workshop on Quality of Experience Management (QoE-Management). , Paris, France (2019).
As stalling is the worst Quality of Experience (QoE) degradation of HTTP adaptive video streaming (HAS), this work presents a stream-based machine learning approach, ViCrypt, which analyzes stalling of YouTube streaming sessions in real-time from encrypted network traffic. The video streaming session is subdivided into a stream of short time slots of 1 s length, while considering two additional macro windows each for the current streaming trend and the whole ongoing streaming session. Constant memory features are extracted from the encrypted network traffic in these three windows in a stream-based fashion, and fed into a random forest model, which predicts whether the current time slot contains stalling or not. The presented system can predict stalling with a very high accuracy and the finest granularity to date (1 s), and thus, can be used in networks for real-time QoE analysis from encrypted YouTube video streaming traffic. The independent predictions for each consecutive slot of a streaming session can further be aggregated to obtain stalling estimations for the whole session. Thereby, the proposed method allows to quantify the initial delay, as well as the overall number of stalling events and the stalling ratio, i.e., the ratio of total stalling time and total playback time.
28.
Wassermann, S., Seufert, M., Casas, P., Li, G., Kuang, L.: I See What you See: Real Time Prediction of Video Quality from Encrypted Streaming Traffic. 4th Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE). , Los Cabos, Mexico (2019).
We address the problem of real-time QoE monitoring of HAS, from the ISP perspective, focusing in particular on videoresolution analysis. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to predict different video resolution levels in a fine-grained scale, ranging from 144p to 1080p resolution, using as input only packet-level data. The proposed measurement system performs predictions in real time, during the course of an ongoing video-streaming session, with a time granularity as small as one second. We consider the particular case of YouTube video streaming. Empirical evaluations on a large and heterogeneous corpus of YouTube measurements demonstrate that the proposed system can predict video resolution with very high accuracy, and in real time. Different from state of the art, the prediction task is not bound to coarse-grained video quality classes and does not require chunk-detection approaches for feature extraction.
29.
Maggi, L., Leguay, J., Seufert, M., Casas, P.: Online Detection of Stalling and Scrubbing in Adaptive Video Streaming. 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt). , Avignon, France (2019).
30.
Seufert, M., Schatz, R., Wehner, N., Gardlo, B., Casas, P.: Is QUIC becoming the New TCP? On the Potential Impact of a New Protocol on Networked Multimedia QoE. 11th International Conference on Quality of Multimedia Experience (QoMEX). , Berlin, Germany (2019).
31.
Seufert, M.: Fundamental Advantages of Considering Quality of Experience Distributions over Mean Opinion Scores. 11th International Conference on Quality of Multimedia Experience (QoMEX). , Berlin, Germany (2019).
32.
Wassermann, S., Casas, P., Seufert, M., Wamser, F.: On the Analysis of YouTube QoE in Cellular Networks through in-Smartphone Measurements. 12th IFIP Wireless and Mobile Networking Conference (WMNC). , Paris, France (2019).
33.
Hirth, M., Lange, S., Seufert, M., Tran-Gia, P.: Performance Evaluation of Mobile Crowdsensing for Event Detection. Proceedings of the 5th International Workshop on Crowd Assisted Sensing, Pervasive Systems and Communications. , Athens, Greece (2018).
Crowdsensing offers a cost effective way to collect large amounts of sensor data. However, in contrast to fixed sensor deployments, the spatial distribution of the sensors can hardly be influence, as the sensors are carried by participants of the crowdsensing system. This in turn raises the question about the performance of such systems with respect to the detection probability and detection time of spatial events. In order to address this question, we analyze the performance of such a crowdsensing system by means of simulation. We use the traffic infrastructure of a small size city in Germany and simulate the inhabitants’ movement patterns with the well established SUMO mobility generator. Our results show that even if only a small share of inhabitants participates in crowdsensing, events, which have locations that are correlated with the population density, can be easily and quickly detected using such a system. On the contrary, events whose locations are uniformly randomly distributed are much harder to detect using a crowdsensing approach.
34.
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).
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.
35.
Schwind, A., Wamser, F., Gensler, T., Seufert, M., Casas, P., Tran-Gia, P.: Streaming Characteristics of Spotify Sessions. The 2nd International Workshop on Quality of Experience Management. , Sardinia, Italy (2018).
Internet Service Providers need a thorough understanding of a service to maximize the Quality of Experience (QoE) of their customers by network management. Instead of quantifying the user satisfaction with long and cost-intensive subjective user studies, the QoE can often be estimated with the help of dedicated measurements of application and network parameters. We designed a QoE measurement tool for the popular audio streaming service Spotify that runs inside a Docker software container. The container is able to run headlessly as active measurement probe and emulates a user who is streaming audio files via Spotify. While streaming, network and application parameters are collected that have a high correlation to the user's QoE. The results of the measurements are used to characterize audio streaming in Spotify on application and network layer, and to evaluate important QoE factors.
36.
Casas, P., Seufert, M., Wehner, N., Schwind, A., Wamser, F.: Enhancing Machine Learning based QoE Prediction by Ensemble Models. 3rd Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE). , Vienna, Austria (2018).
The number of smartphones connected to wireless networks and the volume of wireless network traffic generated by such devices have dramatically increased in the last few years, making it more challenging to tackle wireless network monitoring applications. The high-dimensionality of network data provided by current smartphone devices opens the door to the massive application of machine learning approaches to improve different wireless networking applications. In this paper we study the specific problem of Quality of Experience (QoE) prediction for popular smartphone apps, using machine learning models and in-smartphone measurements. We evaluate and compare different models for the analysis of smartphone generated data, including single models as well as machine learning ensembles such as bagging, boosting and stacking. Results suggest that, while decision-tree based models are the most accurate single models to predict QoE, ensemble learning models, and in particular stacking ones, are capable to significantly increase accuracy prediction and overall classification performance.
37.
Dinh-Xuan, L., Seufert, M., Wamser, F., Vassilakis, C., Zafeiropoulos, A., Tran-Gia, P.: Performance Evaluation of Service Functions Chain Placement Algorithms in Edge Cloud. 30th International Teletraffic Congress (ITC30). , Vienna, Austria (2018).
The emergence of Network Function Virtualization (NFV) paradigm has become a potential solution dealing with the rapid growth of the global Internet traffic in the last decades. There, network appliances are transformed into Virtual Network Functions (VNF) running on a standard server. This promises to significantly reduce overall cost and energy consumption. Additionally, hardware-based network function chain is replaced by a chain of the VNFs, called Service Function Chain (SFC). The expected benefit of SFC is the reduction in the complexity when deploying heterogeneous network services. However, the considerable drawback of SFC is the distribution of the VNFs over different hosts. An inefficient placement of VNFs can induce a high latency within the chain and wasted server resources. In this work, we propose four placement algorithms that aim to efficiently place the SFC in servers with regard to minimizing service response time and resource utilization. Herein, heuristic approaches are evaluated against optimal solutions for the placement problems, which are formulated by using Integer Linear Programming. We evaluate and compare these placement strategies in a simulator. Our result shows that the optimized solutions produce lowest service response time and least server utilization in all types of simulated SFCs. On the other hand, the heuristic algorithms are also able to come close to the optimum by simple placing rules.
38.
Seufert, M., Wehner, N., Casas, P., Wamser, F.: A Fair Share for All: Novel Adaptation Logic for QoE Fairness of HTTP Adaptive Video Streaming. 14th International Conference on Network and Service Management (CNSM), Best Paper Award. , Rome, Italy (2018).
This paper presents a novel adaptation logic for HTTP adaptive streaming (HAS), which achieves not only a high Quality of Experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known Transmission Control Protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this work, the design of the algorithm is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS adaptation logics.
39.
Wehner, N., Wassermann, S., Casas, P., Seufert, M., Wamser, F.: Beauty is in the Eye of the Smartphone Holder - A Data Driven Analysis of YouTube Mobile QoE. 14th International Conference on Network and Service Management (CNSM). , Rome, Italy (2018).
Measuring the Quality of Experience (QoE) undergone by cellular network users has become paramount for cellular ISPs. Given its overwhelming dominance and ever-growing popularity, this paper focuses on the analysis of QoE for YouTube in mobile networks. Using a large-scale dataset of crowdsourced YouTube QoE measurements collected in smartphones with YoMoApp, we analyze the evolution of multiple relevant QoE-related metrics over time for YouTube mobile users. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our data-driven analysis shows a systematic performance and QoE improvement of YouTube in mobile devices over time, accompanied by an improvement of cellular network performance and by an optimization of the YouTube streaming behavior for smartphones.
40.
Seufert, M., Schwind, A., Waigand, M., Hoßfeld, T.: Potential Traffic Savings by Leveraging Proximity of Communication Groups in Mobile Messaging. 14th International Conference on Network and Service Management (CNSM). , Rome, Italy (2018).
Communication groups in mobile messaging applications (MMAs) multiply the data transmissions, because every message has to be delivered to all members of the communication group. Thereby, they put a high load on mobile networks. As the number of recipients is still comparably small, the dataintensive user-generated content cannot be handled efficiently in large content delivery networks. However, small communication groups, such as groups of friends or teams, might often be in close proximity, which can be leveraged to locally deliver messages by applying edge caching or device-to-device (D2D) communication. In this work, a simulation study is conducted to investigate these potential traffic savings in the mobile network. It is based on a realistic communication model of the MMA WhatsApp and utilizes different models for human mobility. The user mobility and MMA communication are simulated for a single day in a small city to obtain the ratio of messages, which could be potentially transmitted locally when utilizing edge caching and D2D communication.
41.
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).
42.
Seufert, M., Tran-Gia, P.: Quality of Experience and Access Network Traffic Management of HTTP Adaptive Video Streaming. IEEE/IFIP Network Operations and Management Symposium (NOMS), Best Dissertation Award. , Taipei, Taiwan (2018).
The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.
43.
Zach, O., Slanina, M., Seufert, M.: Investigating the Impact of Advertisement Banners and Clips on Video QoE. 3rd Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE). , Vienna, Austria (2018).
Although Quality of Experience (QoE) of Internet services can be affected by context influence factors, their actual impact is not widely investigated yet. In the context of online video services, web portals often display advertisement banners or clips to monetize their service. However, these advertisements can distract or annoy the users, which might degrade the QoE of the actual video service. In this work, two crowdsourcing studies were conducted to investigate the impact of advertisement banners and clips on video QoE. Therefore, both theoretical opinions on in-service advertisements and subjective quality ratings are evaluated. The findings confirm that advertisements are negatively perceived by users during service consumption, but a generally negative impact on video QoE cannot be supported, as the interplay of advertisement and the QoE of video services is rather complex.
44.
Seufert, M., Wehner, N., Casas, P.: Studying the Impact of HAS QoE Factors on the Standardized QoE Model P.1203. 3rd Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE). , Vienna, Austria (2018).
P.1203 is a recent standardized model for assessing the Quality of Experience (QoE) of HTTP Adaptive Video Streaming (HAS). However, its complex definition does not allow for a straightforward identification of the underlying assumptions. To overcome this issue, this work investigates the impact of the well-known QoE factors of HAS, namely, initial delay, stalling, and adaptation, on the output QoE score of the model. Therefore, parameter studies are conducted using a reference implementation of P.1203, and the model response to variations of the input QoE factors are compared to results of previous QoE studies in order to get a deeper understanding of the standardized model and its inherent weighting of the QoE factors of HAS.
45.
Burger, V., Seufert, M., Zinner, T., Tran-Gia, P.: An Approximation of the Backhaul Bandwidth Aggregation Potential Using a Partial Sharing Scheme. 15th IFIP/IEEE International Symposium on Integrated Network Management (IM). , Lisbon, Portugal (2017).
To cope with the increasing demands of mobile devices and the limited capacity of cellular networks, mobile connections are offloaded to WiFi. The access capacity is further increased by aggregating backhaul bandwidth of WiFi access links. To analyze the performance of aggregated access links we develop a model for two and more cooperating systems sharing capacities using an offloading scheme. The state probabilities of the different cooperating systems in the analytic model are determined by a fixed point iterative procedure. By investigating an inner and outer composite system we are able to analyze the system in imbalanced load conditions where the system reaches its full potential utilizing spare bandwidth. To evaluate the robustness of the system against users that try to exploit the system, the bandwidth received by prioritized users is quantified.
46.
Dinh-Xuan, L., Seufert, M., Wamser, F., Tran-Gia, P.: Study on the Accuracy of QoE Monitoring for HTTP Adaptive Video Streaming Using VNF. 1st IFIP/IEEE International Workshop on Quality of Experience Management (QoE-Management). , Lisbon, Portugal (2017).
The fast growth of HTTP video streaming is responsible for a huge amount of traffic over the past few years. Due to the variety and popularity of video content, more and more people are watching videos on the smart TV or on mobile devices. As a result, a potential market is emerging for video providers, which can significantly increase their revenues. In order to offer users a good experience, adaptive video streaming has been introduced to adapt the video quality to the network conditions. Nevertheless, it is still difficult for the network operators to assess the actual video quality on the device of the users and therefore they can not react to improve the service on the network. In this work, we propose a Virtual Network Function (VNF) to monitor the Quality of Experience (QoE) for online video service in the network. To conduct the study, on the one hand, we design a VNF monitoring to measure the video quality and estimate the QoE at the client machine. Our function is placed in two locations nearby and far away from the user to analyze the impact of geographical placement of the VNF on its performance. On the other hand, we set up a local testbed to examine the functional operation and measure the actual video buffer from a client web browser directly to validate the accuracy of the function. Our findings show that with respect to function placement, the VNF has high accuracy in estimating the QoE if it is deployed at the edge network close to the user. However, the VNF does not perform well when it operates far away from the users, e.g., at data centers. These insights help network vendors to more closely monitor the quality of the videos streamed to their customers.
47.
Casas, P., D’Alconzo, A., Wamser, F., Seufert, M., Gardlo, B., Schwind, A., Tran-Gia, P., Schatz, R.: Predicting QoE in Cellular Networks using Machine Learning and in-Smartphone Measurements. 9th International Conference on Quality of Multimedia Experience (QoMEX). , Erfurt, Germany (2017).
Monitoring the Quality of Experience (QoE) undergone by cellular network customers has become paramount for cellular ISPs, who need to ensure high quality levels to limit customer churn due to quality dissatisfaction. This paper tackles the problem of QoE monitoring, assessment and prediction in cellular networks, relying on end-user device (i.e., smartphone) QoS passive traffic measurements and QoE crowdsourced feedback. We conceive different QoE assessment models based on supervised machine learning techniques, which are capable to predict the QoE experienced by the end user of popular smartphone apps (e.g., YouTube and Facebook), using as input the passive in-device measurements. Using a rich QoE dataset derived from field trials in operational cellular networks, we benchmark the performance of multiple machine learning based predictors, and construct a decision-tree based model which is capable to predict the per-user overall experience and service acceptability with a success rate of 91% and 98% respectively. To the best of our knowledge, this is the first paper using end-user, in-device passive measurements and machine learning models to predict the QoE of smartphone users in operational cellular networks.
48.
Zach, O., Seufert, M., Hirth, M., Slanina, M., Tran-Gia, P.: On Use of Crowdsourcing for H.264/AVC and H.265/HEVC Video Quality Evaluation. Radioelektronika. , Brno, Czech Republic (2017).
Crowdsourcing has become a popular method in the field of video quality evaluation. Gathering the opinion of the users using crowdsourcing is quick and relatively cheap but such a study has to be designed very carefully in order to give relevant results. So far, the majority of the QoE studies using crowdsourcing has been focusing on the performance of H.264/AVC algorithm in different situations (such as encoder settings, stalling effects, etc). Modern video coding methods, however, are only rarely tested using the crowdsourcing approach. We designed a study comparing the performance of both H.264/AVC and H.265/HEVC standards in the crowdsourcing environment. We deal with the possibilities of delivering and presenting the HEVC encoded content to the participants of the crowdsourcing study and potential challenges. Finally, the study was performed using Microworkers platform and gathered results are then compared with three different objective video quality metrics.
49.
Seufert, M., Wehner, N., Wamser, F., Casas, P., D’Alconzo, A., Tran-Gia, P.: Unsupervised QoE Field Study for Mobile YouTube Video Streaming with YoMoApp. 9th International Conference on Quality of Multimedia Experience (QoMEX). , Erfurt, Germany (2017).
YoMoApp (YouTube Monitoring App) is an Android app to monitor mobile YouTube video streaming on both application- and network-layer. Additionally, it allows to collect subjective Quality of Experience (QoE) feedback of end users. During the development of the app, the stable versions of YoMoApp were already available in the Google Play Store, and the app was downloaded, installed, and used on many devices to monitor streaming sessions. As the app was not advertised in special campaigns or used for dedicated QoE studies, the monitored streaming sessions of this period compose the data set of a large unsupervised field study. The collected data set is evaluated to characterize current mobile YouTube streaming on both application and network layers. Furthermore, the problems and methodology to obtain QoE results from such unsupervised field study are discussed together with the actual QoE results. Correlations between QoE factors are investigated, and the QoE of clusters of similar streaming sessions is analyzed.
50.
Seufert, M., Lange, S., Meixner, M.: Automated Decision Making based on Pareto Frontiers in the Context of Service Placement in Networks. 29th International Teletraffic Congress (ITC). , Genoa, Italy (2017).
Virtualization paradigms like cloud computing, software defined networking (SDN), and network functions virtualization (NFV) provide advantages with respect to aspects like flexibility, costs, and scalability. However, management and orchestration of the resulting networks also introduce new challenges. The placement of services, such as virtual machines~(VMs), virtualized network functions~(VNFs), or SDN controllers, is a multi-objective optimization task that confronts operators with a multitude of possible solutions that are incomparable among each other. The goal of this work is to investigate mechanisms that enable automated decision making between such multi-dimensional solutions. To this end, we investigate techniques from the domain of multi-attribute decision making that aggregate the performance of placements to a single numeric score. A comparison between resulting rankings of placements shows that many techniques produce similar results. Hence, placements that achieve good rankings according to many approaches might be viable candidates in the context of automated decision making. In order to illustrate the functionality of the different scoring mechanisms, we perform a case study on a single network graph and a fixed number of objectives and service instances. Additionally, we present aggregated results from broad evaluations on the Internet Topology Zoo and a larger number of objectives as well as varying numbers of service instances. These allow making more reliable statements about the mechanisms' performance and agreement.
51.
Seufert, M., Zach, O., Slanina, M., Tran-Gia, P.: Unperturbed Video Streaming QoE Under Web Page Related Context Factors. 9th International Conference on Quality of Multimedia Experience (QoMEX). , Erfurt, Germany (2017).
Quality of Experience (QoE) of Internet services is affected by human, system, and context influence factors. While most QoE studies so far are focused on system factors only, this work will assess the impact of context factors of video streaming on QoE. As video streaming is mostly consumed from web pages, such as video portals, the investigated test conditions are applied to the web page, which embeds the video player. Therefore, the study of context factors is implicitly conducted within a crowdsourced QoE study. The test conditions considered different page load times, poster image qualities, and displayed advertisements on the web page, which are typical context factors when consuming a video streaming service. The results of the study show that the modification of the context factors on the streaming web page leaves the users' QoE rating unperturbed, which suggests that the investigated context factors have a negligible impact on video streaming QoE, or that the rating task of the subjective QoE study superimposed the context factors.
52.
Seufert, M., Kamneng Kwam, B., Wamser, F., Tran-Gia, P.: EdgeNetworkCloudSim: Placement of Service Chains in Edge Clouds Using NetworkCloudSim. 1st IEEE International Workshop on Network Programmability - From the Data Center to the Ground (NetFoG). , Bologna, Italy (2017).
Edge cloud computing is a trending paradigm, which extends cloud computing by additionally utilizing computing resources at the network edge, e.g., at mobile base stations. Especially personalized services can be instantiated or migrated close to end users, which improves the latency and supports user mobility. However, the placement of the service chains is crucial for the performance of the services and the energy consumption of the edge cloud platform, and appropriate algorithms have to be designed. To support the simulative performance evaluation of such algorithms, EdgeNetworkCloudSim was developed. It is an extension of NetworkCloudSim, and allows to simulate and evaluate the orchestration and consolidation of service chains in an edge network cloud.
53.
Wamser, F., Höfner, S., Seufert, M., Tran-Gia, P.: Server and Content Selection for MPEG DASH Video Streaming with Client Information. ACM SIGCOMM Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE). , Los Angeles, CA, USA (2017).
In HTTP adaptive streaming (HAS), such as MPEG DASH, the video is split into chunks and is available in different quality levels. If the video chunks are stored or cached on different servers to deal with the high load in the network and the Quality of Experience (QoE) requirements of the users, the problem of content selection arises. In this paper, we evaluate client-side algorithms for dynamically selecting an appropriate content server during DASH video streaming. We present three algorithms with which the DASH client itself can determine the most appropriate server based on client-specific metrics, like actual latency or bandwidth to the content servers. We evaluate and discuss the proposed algorithms with respect to the resulting DASH streaming behavior in terms of buffer levels and quality level selection.
54.
Schwind, A., Seufert, M., Alay, Özgü, Casas, P., Tran-Gia, P., Wamser, F.: Concept and Implementation of Video QoE Measurements in a Mobile Broadband Testbed. IEEE/IFIP Workshop on Mobile Network Measurement (MNM’17). , Dublin, Ireland (2017).
The MONROE testbed enables the objective performance assessment of MBB networks from the end-user perspective, using highly distributed measurements from fixed and mobile nodes. To quantify the performance of MBB networks for popular Internet services from a user-centric perspective, dedicated tools are needed. In this paper we extend the MONROE testbed to the Quality of Experience (QoE) domain, presenting the design and implementation of a QoE-capable measurement tool for YouTube video streaming. The measurement concept is based on emulating a virtual end-user device requesting video streams, which are then monitored at the network and application layers, on the basis of QoE-relevant features. The initial measurements conducted in the MONROE testbed and reported in this paper demonstrate the applicability of the implemented measurement concept.
55.
Seufert, M., Moldovan, C., Burger, V., Hoßfeld, T.: Applicability and Limitations of a Simple WiFi Hotspot Model for Cities. 13th International Conference on Network and Service Management (CNSM). , Tokio, Japan (2017).
Offloading mobile Internet data via WiFi has emerged as an omnipresent trend. WiFi networks are already widely deployed by many private and public institutions (e.g., libraries, cafes, restaurants) but also by commercial services to provide alternative Internet access for their customers and to mitigate the load on mobile networks. Moreover, smart cities start to install WiFi infrastructure for current and future civic services, e.g., based on sensor networks or the Internet of Things. A simple model for the distribution of WiFi hotspots in an urban environment is presented. The hotspot locations are modeled with a uniform distribution of the angle and an exponential distribution of the distance, which is truncated to the city limits. We compare the characteristics of this model in detail to the real distributions. Moreover, we show the applicability and the limitations of this model, and the results suggest that the model can be used in scenarios, which do not require an accurate spatial collocation of the hotspots, such as offloading potential, coverage, or signal strength.
56.
Nguyen-Ngoc, A., Lange, S., Zinner, T., Seufert, M., Tran-Gia, P., Aerts, N., Hock, D.: Performance Evaluation of Selective Flow Monitoring in the ONOS Controller. 4th International Workshop on Management of SDN and NFV Systems (ManSDN/NFV). , Tokio, Japan (2017).
One of the benefits when network operators adopt the Software Defined Networking (SDN) paradigm is the ability to monitor the traffic in the network without an additional network management system. Usually, SDN controllers utilize OpenFlow statistics messages in order to regularly gather information about all flows in the network. However, using the same polling interval for all flows does not take into account the heterogeneity of real world traffic and thus results in an imbalance between monitoring accuracy and control plane overhead. In particular, frequent querying results in a high resource consumption at the controller. This work proposes a Selective Flow Monitoring (SFM) mechanism that allows administrators to classify flows according to their individual requirements in terms of monitoring frequency, e.g., less frequent polling of elephant flows and frequent polling of QoS sensitive VoIP connections. We compare the performance of the SFM mechanism with the default monitoring scheme in a testbed featuring the Open Network Operating System (ONOS) controller. In this context, the CPU utilization of the controller is used as performance indicator. After identifying relevant influence factors like the number of flows and switches in the network, we investigate the viability of the approaches in different scenarios. Finally, we provide guidelines regarding their choice.
57.
Seufert, M., Hoßfeld, T., Schwind, A., Burger, V., Tran-Gia, P.: Group-based Communication in WhatsApp. 1st IFIP Internet of People Workshop (IoP). , Vienna, Austria (2016).
WhatsApp is a very popular mobile messaging application, which dominates today’s mobile communication. Especially the feature of group chats contributes to its success and changes the way people communicate. The group-based communication paradigm is investigated in this work, particularly focusing on the usage of WhatsApp, communication in group chats, and implications on mobile network traffic.
58.
Burger, V., Frances Pajo, J., Sanchez, O.R., Seufert, M., Schwartz, C., Wamser, F., Davoli, F., Tran-Gia, P.: Load Dynamics of a Multiplayer Online Battle Area and Simulative Assessment of Edge Server Placements. ACM Multimedia Systems Conference (MMSys). , Klagenfurt, Austria (2016).
Free-to-play models, streaming of games and eSports are reasons for online gaming to grow in popularity recently. On the forefront are multiplayer online battle arenas, which gain high popularity by introducing a competitive format that is easy to access and requires cooperation and team play. These games highly rely on fast reaction of the players, which makes latency the key performance indicator of such applications. To obtain low latency, this paper proposes moving game servers close to players towards the edge of the network. The performance of such mechanism highly depends on the geographic distribution of players. By analyzing match histories and statistics, we develop models for the arrival process and location of game requests. This allows us to evaluate the performance of edge server resource migration policies in an event based simulation. Our results show that a high number of edge servers is preferable compared to few larger edge servers to reduce the latency of players. This supports approaches that allow deploying virtual server instances in the back-haul.
59.
Seufert, M., Casas, P., Wamser, F., Wehner, N., Schatz, R., Tran-Gia, P.: Application-Layer Monitoring of QoE Parameters for Mobile YouTube Video Streaming in the Field. IEEE 6th International Conference on Communications and Electronics (ICCE). , Ha Long, Vietnam (2016).
YouTube video streaming is one of the most popular and most demanding services in cellular networks. Thus, operators are concerned about the quality of the streaming delivered by their networks and would like to monitor the Quality of Experience (QoE) of the end users. In this work, we conduct a field study of mobile YouTube video streaming, in which both network flow parameters and application-layer streaming parameters were monitored, and present the characteristics of current mobile YouTube streaming. The impact of both approaches is investigated showing that monitoring network parameters is not sufficient to directly infer the resulting QoE. In contrast, the streaming parameters, which can be obtained from application-layer monitoring, show high correlations to the subjectively experienced quality, and thus, are better suited for QoE monitoring.
60.
Seufert, M., Zach, O., Hoßfeld, T., Slanina, M., Tran-Gia, P.: Impact of Test Condition Selection in Adaptive Crowdsourcing Studies on Subjective Quality. 8th International Conference on Quality of Multimedia Experience (QoMEX). , Lisbon, Portugal (2016).
Adaptive crowdsourcing is a new approach to crowdsourced Quality of Experience (QoE) studies, which aims to improve the certainty of resulting QoE models by adaptively distributing a fixed budget of user ratings to the test conditions. The main idea of the adaptation is to dynamically allocate the next rating to a condition, for which the submitted ratings so far show a low certainty. This paper investigates the effects of statistical adaptation on the distribution of ratings and the goodness of the resulting QoE models. Thereby, it gives methodological advice how to select test conditions for future crowdsourced QoE studies.
61.
Seufert, M., Lange, S., Meixner, M.: Automated Decision Making Methods for the Multi-objective Optimization Task of Cloud Service Placement. 1st International Workshop on Programmability for Cloud Networks and Applications (PROCON). , Würzburg, Germany (2016).
The network functions virtualization (NFV) paradigm provides advantages with respect to aspects like flexibility, costs, and scalability of networks. However, management and orchestration of the resulting networks also introduce new challenges. The placement of services and virtualized network functions (VNFs) is a multi-objective optimization task that confronts operators with a multitude of possible solutions that are incomparable among each other. The goal of this work is to investigate mechanisms that enable automated decision making between such multi dimensional solutions. To this end, we investigate techniques from the domain of multi attribute decision making that aggregate the performance of placements to a single numeric score. A comparison between resulting rankings of placements shows that many techniques produce similar results. Hence, placements that achieve good rankings according to many approaches might be viable candidates in the context of automated decision making.
62.
Seufert, M., Hoßfeld, T.: One Shot Crowdtesting: Approaching the Extremes of Crowdsourced Subjective Quality Testing. 5th ISCA/DEGA Workshop on Perceptual Quality of Systems (PQS). , Berlin, Germany (2016).
Crowdsourcing studies for subjective quality testing have become a particularly useful tool for Quality of Experience researchers. Typically, crowdsouring studies are conducted by many unsupervised workers, which rate the perceived quality of several test conditions during one session (mixed within-subject test design). However, those studies often show to be very sensitive, for example, to test instructions, design, and filtering of unreliable participants. Moreover, the exposure of several test conditions to single workers potentially leads to an implicit training and anchoring of ratings. Therefore, this works investigates the extreme case of presenting only a single test condition to each worker (completely between-subjects test design). The results are compared to a typical crowdsourcing study design with multiple test conditions to discuss training effects in crowdsourcing studies. Thus, this work investigates if it is possible to use a simple ``one shot'' design with only one rating of a large number of workers instead of sophisticated (mixed or within-subject) test designs in crowdsourcing.
63.
Wamser, F., Seufert, M., Höfner, S., Tran-Gia, P.: Concept for Client-initiated Selection of Cloud Instances for Improving QoE of Distributed Cloud Services. ACM SIGCOMM Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE). , Florianópolis, Brazil (2016).
We introduce a concept for client-initiated selection of service location and service quality for improving the Quality of Experience (QoE) of general cloud services. It is loosely based on the HTTP adaptive streaming approach (e.g., MPEG DASH). A manifest file compiled by the cloud service provider specifies the available service locations and qualities, from which the user selects the optimal service instance based on contextual information obtained from client measurements and user preferences. The proposed concept is defined and is implemented in two client-based decision algorithms for improving the QoE of a simple picture gallery cloud service. These decision algorithms are evaluated and their impact on the service delivery is discussed. The evaluation shows that it is possible to improve the service location and quality selection by light-weight client-based algorithms.
64.
Dinh-Xuan, L., Seufert, M., Wamser, F., Tran-Gia, P.: QoE Aware Placement of Content in Edge Networks on the Example of a Photo Album Cloud Service. IEEE 6th International Conference on Communications and Electronics (ICCE). , Ha Long, Vietnam (2016).
The paradigm of Software as a Service has gained great achievements in the last decade. By transferring computation and storage to the cloud and migrating services to the edge network, users benefit from using demanding services on lightweight devices. However, the user perceived quality of experience (QoE) for these services is facing the challenges of network impairments and the accessibility of users. Unlike a typical PC-based software, the cloud provides users a location-aware, flexible placement of resource for a cost effective service. The geographical placement of content is therefore one of the key factors that affects the user's satisfaction. The closer the content to the user geographically is, the faster it will be delivered to the user that will also increase the user perceived QoE. In this work, we estimate more precisely the QoE for photo loading time in a particular usage of a photo album cloud service with regard to the influence of various parameters. Firstly, we validate a TCP throughput model and use it to calculate the photo loading time from a given photo size and network QoS. Thereafter, we formulate a mapping function to calculate the MOS value from a QoE model adding the output of the TCP model. From this mapping function, we can estimate QoE for photo loading time from a given photo size, its placement and network QoS. Our main contribution is to determine the trade-off between the size of photo and its placement to acquire a high QoE for photo loading time, which is important for the development of a photo album cloud service.
65.
Metter, C., Seufert, M., Wamser, F., Zinner, T., Tran-Gia, P.: Analytic Model for SDN Controller Traffic and Switch Table Occupancy. 12th International Conference on Network and Service Management (CNSM). , Best Paper Award, Montreal, Canada (2016).
Software Defined Networking (SDN) is a major paradigm in the field of current communication networks. SDN is used as the basis of many new networks although few performance models are available in the literature, and the majority of performance evaluations are based primarily on practical measurements. To fill this gap, we develop an analytical model to assess SDN control plane traffic as well as the occupancy of the flow table of an SDN switch. The contribution of this work is the formulation of the model for the performance-decisive parameters control-plane traffic and flow table occupancy and the application of the model for different data plane traffic characteristics. In the end, there is a discussion about the setting of time-out values for storing flow entries in the switch flow table depending on the traffic characteristics in the data plane. The trade-off between the signaling traffic in the control plane and the occupancy of the flow table is discussed in order to minimize both.
66.
Seufert, M., Burger, V., Kaup, F.: Evaluating the Impact of WiFi Offloading on Mobile Users of HTTP Adaptive Video Streaming. 5th IEEE International Workshop on Quality of Experience for Multimedia Communications (QoEMC). , Washington, DC, USA (2016).
In a recent trend to lessen the load on cellular networks in cities, users are offered to offload mobile connections to lower cost WiFi networks. In this work, we conduct a simulative performance evaluation of the impact of WiFi offloading for a mobile end user of a HTTP adaptive video streaming (HAS) service depending on availability and range of the WiFi hotspots. The simulation is based on connectivity measurements from a German city and evaluates the key performance indicators for the QoE of HAS, i.e., initial delay, stalling, and quality adaptation. Additionally, a smartphone energy model is applied to assess the energy consumption during the streaming. The results indicate that WiFi offloading of HAS connections to public WiFi hotspots is not attractive for end users both in terms of QoE and energy consumption. However, it can be shown that WiFi offloading can be beneficial also for end users in case high bandwidths can be received via WiFi.
67.
Burger, V., Seufert, M., Kaup, F., Wichtlhuber, M., Hausheer, D., Tran-Gia, P.: Impact of WiFi Offloading on Video Streaming QoE in Urban Environments. IEEE Workshop on Quality of Experience-based Management for Future Internet Applications and Services (QoE-FI). , London, UK (2015).
Video streaming is the most popular application in today's mobile Internet and its growing demands and popularity put more and more load on cellular networks. In a recent trend to mitigate the cellular load, followed by many providers, users are offered to offload mobile connections to WiFi hotspots, which are predominately deployed in urban environments. In this work, we conduct a simulative performance evaluation of the impact of WiFi offloading on the Quality of Experience (QoE) of video streaming. The evaluation is based on connectivity measurements from a German city and uses a simple QoE model for estimating the perceived quality of video streaming. Our findings show that, despite its benefits for operators, offloading to WiFi has a negative impact on video streaming QoE for some users when 3G/4G coverage is available. Only in the case of 2G coverage, WiFi offloading can significantly improve the perceived quality for users.
68.
Wamser, F., Seufert, M., Casas, P., Irmer, R., Tran-Gia, P., Schatz, R.: YoMoApp: a Tool for Analyzing QoE of YouTube HTTP Adaptive Streaming in Mobile Networks. European Conference on Networks and Communications (EuCNC). , Paris, France (2015).
The performance of YouTube in mobile networks is crucial to network operators, who try to find a trade-off between cost-efficient handling of the huge traffic amounts and high perceived end-user Quality of Experience (QoE). This paper introduces YoMoApp (YouTube Performance Monitoring Application), an Android application, which passively monitors key performance indicators (KPIs) of YouTube adaptive video streaming on end-user smartphones. The monitored KPIs (i.e., player state/events, buffer, and video quality level) can be used to analyze the QoE of mobile YouTube video sessions. YoMoApp is a valuable tool to assess the performance of mobile networks with respect to YouTube traffic, as well as to develop optimizations and QoE models for mobile HTTP adaptive streaming. We test YoMoApp through real subjective QoE tests showing that the tool is accurate to capture the experience of end-users watching YouTube on smartphones.
69.
Seufert, M., Burger, V., Wamser, F., Tran-Gia, P., Moldovan, C., Hoßfeld, T.: Utilizing Home Router Caches to Augment CDNs towards Information-Centric Networking. European Conference on Networks and Communications (EuCNC). , Paris, France (2015).
To implement improved Quality of Service (QoS) and Quality of Experience (QoE) management for content-heavy services like video streaming, content has to be moved closer to the edge. The concept of information-centric networking (ICN) would be a prospective enabler but is currently not practically feasible yet. We propose a hierarchical caching architecture utilizing caches on home routers to augment existing content delivery network (CDN) infrastructure. This approach can be implemented via Software-defined Networking (SDN) and brings current CDNs closer towards ICN. Based on a simulation study, we confirm that our approach is able to serve content more locally, which results in QoS and QoE benefits for end users as well as inter-domain traffic savings for network operators.
70.
Casas, P., Schatz, R., Wamser, F., Seufert, M., Irmer, R.: Exploring QoE in Cellular Networks: How Much Bandwidth do you Need for Popular Smartphone Apps?. 5th ACM SIGCOMM Workshop on All Things Cellular: Operations, Applications and Challenges. , London, UK (2015).
A quarter of the world population will be using smartphones to access the Internet in the near future. In this context, understanding the Quality of Experience (QoE) of popular services in such devices becomes paramount for cellular network operators, who need to offer high quality levels to reduce the risks of customers churning for quality dissatisfaction. In this paper we study the problemof QoE provisioning in smartphones, presenting the results obtained from subjective lab tests performed for five popular apps: YouTube, Facebook, Web browsing through Chrome, Google Maps, and WhatsApp. The analysis addresses the impact of the access downlink bandwidth on the QoE of these apps when accessed through smartphones. The results presented in this paper provide a sound basis for better understanding the QoE requirements of popular services and mobile apps, as well as for dimensioning the underlying provisioning network. To the best of our knowledge, this is the first paper providing such a comprehensive analysis of QoE in mobile devices.
71.
Lareida, A., Petropoulos, G., Burger, V., Seufert, M., Soursos, S., Stiller, B.: Augmenting Home Routers for Socially-Aware Traffic Management. 40th Annual IEEE Conference on Local Computer Networks (LCN). , Clearwater Beach, FL, USA (2015).
Mobile users' Quality-of-Experience (QoE) is degrading as network usage increases while Internet Service Providers (ISP) face increased inter-domain traffic. This paper presents a network traffic management mechanism, named RB-HORST, addressing these inefficiencies. RB-HORST exploits home routers by using them as caches and forming an overlay network between them to transfer content. To shift traffic from peak hours, RB-HORST employs predictions based on social network properties and based on similarity in the overlay network. To further improve user QoE, home routers allow trusted mobile devices to offload their mobile connection to the local WiFi. Simulation results show that an overlay is imperative for the success of the proposed caching mechanism. Especially ISPs with a large number of customers can benefit if only every thousandth user shares its router, reducing inter-domain traffic by half and superseding an ISP operated cache. The presented implementation proves that the concept is technically feasible and can be deployed and run on constrained devices.
72.
Seufert, M., Wamser, F., Casas, P., Irmer, R., Tran-Gia, P., Schatz, R.: YouTube QoE on Mobile Devices: Subjective Analysis of Classical vs. Adaptive Video Streaming. 6th International Workshop on Traffic Analysis and Characterization (TRAC). , Dubrovnik, Croatia (2015).
YouTube is the most popular service in the Internet and is increasingly consumed on mobile devices. With emerging adaptive video streaming technology, the question arises whether it should be also employed in the mobile context, which shows different characteristics in terms of display sizes and reliability of Internet connection. This paper compares YouTube QoE on mobile devices for both classical and adaptive video streaming based on a subjective lab experiment, in which different network conditions were emulated. Our results show that adaptive video streaming provides almost excellent results for the poorest network conditions. Thereby, it clearly outperforms classical video streaming, and thus, should be considered to achieve higher QoE in future mobile streaming applications.
73.
Seufert, M., Schwind, A., Hoßfeld, T., Tran-Gia, P.: Analysis of Group-based Communication in WhatsApp. 7th EAI International Conference on Mobile Networks and Management (MONAMI). , Santander, Spain (2015).
This work investigates group-based communication in WhatsApp based on a survey and the analysis of messaging logs. The characteristics of WhatsApp group chats in terms of usage and topics are outlined. We present a classification based on the topic of the group and classify anonymized messaging logs based on message statistics. Finally, we model WhatsApp group communication with a semi-Markov process, which can be used to generate network traffic similar to real messaging logs.
74.
Burger, V., Darzanos, G., Papafili, I., Seufert, M.: Trade-Off between QoE and Operational Cost in Edge Resource Supported Video Streaming. 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). , Krakow, Poland (2015).
The largest share of today`s consumer Internet traffic is video streaming and its demand on content delivery networks is continuously growing. To cope with the increasing demand of video streaming, recent work proposes mitigating end-user equipment to support content delivery at the edge of the network. The throughput of end-user equipment supporting content delivery is limited by the uplink of the users Internet connection.Especially for video streaming insufficient throughput causes the video to stall and affects the Quality of Experience (QoE) of end-users.To prevent video streams from stalling, we consider a tiered caching architecture, which requests higher tier caches to support content delivery, if the uplink throughput drops below a certain threshold. We conduct a simulative performance evaluation of the mechanism to investigate its impact on the QoE of end-users. Our results show that especially if the upload bandwidth of end-user equipment is low the setting of the threshold has a high impact. This can be used by operators to achieve the desired trade-off between QoE and operational cost for cache resources.
75.
Seufert, M., Hoßfeld, T., Sieber, C.: Impact of Intermediate Layer on Quality of Experience of HTTP Adaptive Streaming. 11th International Conference on Network and Service Management (CNSM). , Barcelona, Spain (2015).
HTTP Adaptive Streaming (HAS) adapts the video quality to the current network condition by switching between different quality layers. As HAS was shown to perform better than classical video streaming, it is becoming increasingly popular. Recent research showed that quality switch amplitude and time on layer have an impact on the Quality of Experience (QoE) of HAS. However, those studies focused only on adaptation between two layers so far. This work extends these findings by taking adaptation between three layers into account. Thereby, especially the impact of an intermediate layer on user perceived quality is investigated. Crowdsourcing experiments were conducted in order to collect subjective ratings for adaptation between three layers. The results indicate that the quality of each layer and the time on each layer are important QoE parameters. This encourages the usage of temporal pooling approaches for QoE prediction and QoE-aware traffic management. Therefore, mean pooling of per-frame metrics will be applied and its performance will be validated with the subjective crowdsourcing results.
76.
Casas, P., Gardlo, B., Seufert, M., Wamser, F., Schatz, R.: Taming QoE in Cellular Networks: from Subjective Lab Studies to Measurements in the Field. 11th International Conference on Network and Service Management (CNSM). , Barcelona, Spain (2015).
A quarter of the world population will be using smartphones to access the Internet in the near future. In this context, understanding the Quality of Experience (QoE) of popular apps in such devices becomes paramount to cellular network operators, who need to offer high quality levels to reduce the risks of customers churning for quality dissatisfaction. In this paper we address the problem of QoE provisioning in smartphones from a double perspective, combining the results obtained from subjective lab tests with end-device passive measurements and QoE crowd-sourced feedback obtained in operational cellular networks. The study addresses the impact of the downlink bandwidth on the QoE of three popular smartphone apps: YouTube, Facebook and Google Maps. As a main contribution, we show that the results obtained in the lab are highly applicable in the live scenario, as mappings track the QoE provided by users in real networks. We additionally provide hints and bandwidth thresholds for good QoE levels on such apps, as well as discussion on end-device passive measurements and analysis. The results presented in this paper provide a sound basis to better understand the QoE requirements of popular mobile apps, as well as for monitoring the underlying provisioning network. To the best of our knowledge, this is the first paper providing such a comprehensive analysis of QoE in mobile devices, combining network measurements with users QoE feedback in lab tests and operational networks.
77.
Burger, V., Kaup, F., Seufert, M., Wichtlhuber, M., Hausheer, D., Tran-Gia, P.: Energy Considerations for WiFi Offloading of Video Streaming. 7th EAI International Conference on Mobile Networks and Management (MONAMI). , Santander, Spain (2015).
The load on cellular networks is constantly increasing. Especially video streaming applications, whose demands and requirements keep growing, put high loads on cellular networks. A solution to mitigate the cellular load in urban environments is offloading mobile connections to WiFi access points, which is followed by many providers recently. Because of the large number of mobile users and devices there is also a high potential to save energy by WiFi offloading. In this work, we develop a model to assess the energy consumption of mobile devices during video sessions. We evaluate the potential of WiFi offloading in an urban environment and the implications of offloading connections on energy consumption of mobile devices. Our results show that, although WiFi is more energy efficient than 3G and 4G for equal data rates, the energy consumption increases with the amount of connections offloaded to WiFi, due to poor data rates obtained for WiFi in the streets. This suggests further deployment of WiFi access points or WiFi sharing incentives to increase data rates for WiFi and energy efficiency of mobile access.
78.
Egger, S., Gardlo, B., Seufert, M., Schatz, R.: The Impact of Adaptation Strategies on Perceived Quality of HTTP Adaptive Streaming. 1st Workshop on Design, Quality and Deployment of Adaptive Video Streaming (VideoNext). , Sydney, Australia (2014).
Changing network conditions like bandwidth fluctuations and resulting bad user experience issues (e.g. video freezes) pose severe challenges to Internet video streaming. To address this problem, an increasing number of video services utilizes HTTP adaptive streaming (HAS). HAS enables service providers to improve Quality of Experience (QoE) and resource utilization by incorporating information from different layers. However, these adaptation possibilities of HAS also introduce new perceivable impairments such as the fluctuation of audiovisual quality levels over time, which in turn lead to novel QoE-related research questions.The main contribution of this paper is the formulation of open research questions as well as a thorough systematic user-centric analysis of different quality adaptation dimensions and strategies. The underlying data has been acquired through two crowdsourcing and one lab study. The results provide guidance w.r.t. which encoding dimensions are combined best for the creation of the adaptation set and what type of adaptation strategy should be used. Furthermore it provides insights on the impact of adaptation frequency and the true QoE gain of adaptation over stallings.
79.
Hoßfeld, T., Seufert, M., Sieber, C., Zinner, T.: Assessing Effect Sizes of Influence Factors Towards a QoE Model for HTTP Adaptive Streaming. 6th International Workshop on Quality of Multimedia Experience (QoMEX). , Singapore (2014).
HTTP Adaptive Streaming (HAS) is employed by more and more video streaming services in the Internet. It allows to adapt the downloaded video quality
