TY - BOOK A1 - Tran-Gia, Phuoc A1 - Hoßfeld, Tobias T1 - Performance Modeling and Analysis of Communication Networks BT - A Lecture Note N2 - This textbook provides an introduction to common methods of performance modeling and analysis of communication systems. These methods form the basis of traffic engineering, teletraffic theory, and analytical system dimensioning. The fundamentals of probability theory, stochastic processes, Markov processes, and embedded Markov chains are presented. Basic queueing models are described with applications in communication networks. Advanced methods are presented that have been frequently used in recent practice, especially discrete-time analysis algorithms, or which go beyond classical performance measures such as Quality of Experience or energy efficiency. Recent examples of modern communication networks include Software Defined Networking and the Internet of Things. Throughout the book, illustrative examples are used to provide practical experience in performance modeling and analysis. Target group: The book is aimed at students and scientists in computer science and technical computer science, operations research, electrical engineering and economics. N2 - Dieses Lehrbuch bietet eine Einführung in gängige Methoden zur Modellbildung und analytische Leistungsbewertung von Kommunikationssystemen. Diese Methoden bilden die Grundlage für Verkehrstheorie und Systemdimensionierung. Die Grundlagen der Wahrscheinlichkeitstheorie, stochastische Prozesse, Markov-Prozesse und eingebettete Markov-Ketten werden vorgestellt. Grundlegende Warteschlangenmodelle werden mit Anwendungen aus Kommunikationsnetzwerken beschrieben. Es werden auch weiterführende Methoden vorgestellt, die in der jüngeren Praxis häufig verwendet wurden, insbesondere zeitdiskrete Analysealgorithmen, oder QoE und Energieeffizienz. Aktuelle Beispiele für moderne Kommunikationsnetze sind Software Defined Networking oder das Internet der Dinge. Im gesamten Buch werden anschauliche Beispiele verwendet, um praktische Erfahrungen in der Leistungsmodellierung und -analyse zu vermitteln. Zielgruppe: Das Buch richtet sich an Studierende und WissenschaftlerInnen aus den Bereichen Informatik und technische Informatik, Operations Research, Elektrotechnik und Wirtschaftswissenschaft. KW - performance modeling KW - Markovian and Non-Markovian systems KW - discrete-time models and analysis KW - communication networks KW - communication network KW - performance evaluation KW - Markov model KW - stochastic processes KW - queueing theory Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-241920 SN - 978-3-95826-152-5 SN - 978-3-95826-153-2 N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, 978-3-95826-152-5, 65,00 Euro. PB - Würzburg University Press CY - Würzburg ET - 1st edition ER - TY - JOUR A1 - Loh, Frank A1 - Poignée, Fabian A1 - Wamser, Florian A1 - Leidinger, Ferdinand A1 - Hoßfeld, Tobias T1 - Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming JF - Sensors N2 - Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance. KW - HTTP adaptive video streaming KW - quality of experience prediction KW - machine learning Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-241121 SN - 1424-8220 VL - 21 IS - 12 ER - TY - JOUR A1 - Wamser, Florian A1 - Seufert, Anika A1 - Hall, Andrew A1 - Wunderer, Stefan A1 - Hoßfeld, Tobias T1 - Valid statements by the crowd: statistical measures for precision in crowdsourced mobile measurements JF - Network N2 - Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user's perspective on a large scale. Here, network measurements are performed directly on the end-users' devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider. KW - mobile networks KW - crowdsourced measurements KW - statistical validity Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-284154 SN - 2673-8732 VL - 1 IS - 2 SP - 215 EP - 232 ER -