Enhancing Transparency: Internet Video Quality Inference from Network Traffic

International audienceThe 2017 FCC Restoring Internet Freedom Order removes the "enhanced" transparency obligations introduced by the 2015 Open Internet Order and aims to return net neutrality policy to transparency rules based on the 2010 Open Internet Order. The ruling states that the burden of additional network performance disclosures exceed the benefits, and that the most salient metrics to report are those that involve consumer quality of experience (QoE) for the applications that they commonly use. Unfortunately, however, internet service providers (ISPs) will typically have difficulty reporting on application performance and QoE metrics, both of which are notoriously difficult to estimate from network traffic. To address this shortcomings, we present the initial development of Network Microscope, a tool that estimates QoE for Internet video streaming from passively collected network traffic. Our system sits inline, on path, and analyzes traffic in real time as it traverses the network to (1) identify which traffic flows belong to a specific video streaming service; (2) estimate critical quality-of-experience metrics for streaming video such as: bitrate, changes in bitrate, startup delay, and rebuffering. When deployed on a commodity embedded device, the tools is suitable for deployment in consumer home networks, as well as near various network endpoints. Because Internet video traffic accounts for majority of the global internet traffic, this approach of passively observing traffic has two significant policy implications: - It reduces the administrative and operational burden on ISPs, because traffic collection and analysis is passive, in-line, and in homes, and does not introduce additional test traffic. - The approach offers application QoE metrics that are complementary to the lower-level network performance metrics that ISPs already collect. We discuss the capabilities of Network Microscope, its initial deployment to over 50 consumer homes, our initial findings concerning the reporting of application QoE metrics, and broader implications for policy surrounding ISP transparency reporting requirements. Network Microscope can provide a deeper understanding of several concepts often discussed both in the context of net neutrality and encouraging competitive forces in the market. First, such a tool can shed more light on the nature of streaming traffic from consumers, including which video streaming services are most popular with consumers, and how those popular services perform on different networks. Measurements based on service-specific usage is particularly meaningful to consumers because consumers often understand their network needs better when it is tied to the applications they use often. More specific information about the performance of popular video services can help consumers make more informed choices about the network services that they purchase. Second, the tool facilitates the analysis of application performance for network traffic at multiple locations along a single end-to-end path, enabling both consumers and regulators to independently verify ISP reports about application performance. Ultimately, the type of information about application performance that our tool exposes can affect consumer decision-making; we explore and discuss these effects, and how they may ultimately interact with switching costs, market competition, and other commercial considerations

Nuevas técnicas para optimizar el tráfico de red utilizando Big Data.

[ES] Con el auge del Big Data, el enfoque hacia el análisis del tráfico de red NTMA supone un gran reto. Cuando se trabaja con redes, es bastante común el uso de software especializado para analizar el tráfico que se genera en internet, el cual nos permite, ente otros, el diagnostico de problemas de red, análisis de protocolos y extracción de información de las tramas donde viajan los datos. Sin embargo, hay información que no se puede ver directamente desde los campos de la trama como por ejemplo la calidad de un video de internet que está reproduciendo un usuario. En este trabajo, obtendremos una forma para identificar y clasificar esta información.[EN] With the rise of big data, the approach to network traffic analysis NTMA poses a great challenge. When you work with networks, it's fairly common the use of software specialized in analysing the traffic that the internet generates, which allows us, among other things, the diagnosis of network problems, analysis of protocols and extraction of information from the data frames. But nevertheless, there's information that you can't see directly from the fields of the data frame like for example the quality of a video that a user is playing. In this research paper, we will obtain a way of identifying and clasifying this information.Liébana Carrascosa, ÁD. (2020). Nuevas técnicas para optimizar el tráfico de red utilizando Big Data. Universitat Politècnia de València. http://hdl.handle.net/10251/157712TFG

Evolução do comportamento dos usuários em serviços de streaming de vídeo em larga escala

Video streaming is responsible for most of the traffic flowing on the Internet nowadays, which leads to massive investments in the infrastructure from the main content providers like Netflix, Youtube, and Hulu. In the past few years, these investments generated multiple changes in the transmission infrastructure, improving the content delivery networks (CDN), enhancing the computer processing power and changing the transmission technology used. The ultimate goal of the content providers when making these investments is to transmit streaming video over the Internet with the best quality as possible to please their clients and stand out from their competitors. In this sense, it is necessary to have metrics capable of capturing the impact imposed on the servers, user behavior, and in the quality of the video to know how changes in the transmission infrastructure affect user engagement. In this context, in this work, we characterize the evolution of transmission infrastructure, user behavior, and quality of experience from live video streaming systems over the Internet, using for this purpose the logs generated from the transmission of the 2014 and 2018 FIFA World Cups that were transmitted by the biggest content provider located in Brazil. Some of the results in this work show that traffic has increased more than 300% and the average public increased about 175%. The arrival rate also changed and now it is more concentrated at the beginning of the 1st and 2nd half of the match. Besides that, user engagement was evaluated using different metrics and, in general, it has increased in the 2018 tournament for all metrics.Streaming de vídeo é o responsável pela maior parte do tráfego na Internet atualmente, o que gera investimentos massivos em infraestrutura por parte dos principais provedores de conteúdo como Netflix, Youtube e Hulu. Ao longo dos últimos anos, esses investimentos geraram diversas modificações na infraestrutura de transmissão como melhora nas redes de distribuição de conteúdo (CDN), aumento do poder computacional e mudanças na tecnologia de transmissão. O objetivo final dos provedores de serviço ao realizar todos esses investimentos é ser capaz de realizar uma transmissão com a maior qualidade possível para agradar seus clientes, de modo que suas plataformas se destaquem de seus concorrentes. Nesse sentido, é necessário ter métricas que representem questões como o impacto causado nos servidores por conta da quantidade grande de clientes, o padrão de comportamento desses clientes e a qualidade do vídeo que está sendo assistido, para que seja possível saber os efeitos das mudanças de infraestrutura no engajamento final do cliente. Nesse contexto, esse trabalho avalia a evolução da infraestrutura de transmissão, do comportamento do cliente e da experiência do usuário de sistemas de streaming ao vivo na Internet, utilizando como objeto de estudo os registros de acesso das transmissões das Copas do Mundo da FIFA de 2014 e 2018 realizadas pelo maior provedor de conteúdo do Brasil. Alguns dos resultados obtidos mostram que o tráfego gerado cresceu mais de 300% e o público médio cerca de 175% nos quatro anos entre os eventos. A taxa de chegada também mudou e passou a ser mais concentrada no início do 1º e 2º tempo do jogo. Além disso, em relação ao engajamento do usuário, foram avaliadas diferentes métricas e, em geral, o engajamento aumentou no torneio de 2018 para todas as métricas utilizadas

Dynamic adaptive video streaming with minimal buffer sizes

Recently, adaptive streaming has been widely adopted in video streaming services to improve the Quality-of-Experience (QoE) of video delivery over the Internet. However, state-of-the-art bitrate adaptation achieves satisfactory performance only with extensive buffering of several tens of seconds. This leads to high playback latency in video delivery, which is undesirable especially in the context of live content with a low upper bound on the latency. Therefore, this thesis aims at pushing the application of adaptive streaming to its limit with respect to the buffer size, which is the dominant factor of the streaming latency. In this work, we first address the minimum buffering size required in adaptive streaming, which provides us with guidelines to determine a reasonable low latency for streaming systems. Then, we tackle the fundamental challenge of achieving such a low-latency streaming by developing a novel adaptation algorithm that stabilizes buffer dynamics despite a small buffer size. We also present advanced improvements by designing a novel adaptation architecture with low-delay feedback for the bitrate selection and optimizing the underlying transport layer to offer efficient realtime streaming. Experimental evaluations demonstrate that our approach achieves superior QoE in adaptive video streaming, especially in the particularly challenging case of low-latency streaming.In letzter Zeit setzen immer mehr Anbieter von Video-Streaming im Internet auf adaptives Streaming um die Nutzererfahrung (QoE) zu verbessern. Allerdings erreichen aktuelle Bitrate-Adaption-Algorithmen nur dann eine zufriedenstellende Leistung, wenn sehr große Puffer in der Größenordnung von mehreren zehn Sekunden eingesetzt werden. Dies führt zu großen Latenzen bei der Wiedergabe, was vor allem bei Live-Übertragungen mit einer niedrigen Obergrenze für Verzögerungen unerwünscht ist. Aus diesem Grund zielt die vorliegende Dissertation darauf ab adaptive Streaming-Anwendung im Bezug auf die Puffer-Größe zu optimieren da dies den Hauptfaktor für die Streaming-Latenz darstellt. In dieser Arbeit untersuchen wir zuerst die minimale benötigte Puffer-Größe für adaptives Streaming, was uns ermöglicht eine sinnvolle Untergrenze für die erreichbare Latenz festzulegen. Im nächsten Schritt gehen wir die grundlegende Herausforderung an dieses Optimum zu erreichen. Hierfür entwickeln wir einen neuartigen Adaptionsalgorithmus, der es ermöglicht den Füllstand des Puffers trotz der geringen Größe zu stabilisieren. Danach präsentieren wir weitere Verbesserungen indem wir eine neue Adaptions-Architektur für die Datenraten-Anpassung mit geringer Feedback-Verzögerung designen und das darunter liegende Transportprotokoll optimieren um effizientes Echtzeit-Streaming zu ermöglichen. Durch experimentelle Prüfung zeigen wir, dass unser Ansatz eine verbesserte Nutzererfahrung für adaptives Streaming erreicht, vor allem in besonders herausfordernden Fällen, wenn Streaming mit geringer Latenz gefordert ist

Mediale Teilhabe: Partizipation zwischen Anspruch und Inanspruchnahme

Gesellschaftliche, politische und wissenschaftliche Forderungen nach mehr Beteiligung, Zugang und Mitwirkung sind ebenso allgegenwärtig wie spannungsgeladen und durchzogen von Ambivalenzen. Mediale Teilhabe fragt nach den medialen Ermöglichungs- und Austauschprozessen, als deren Effekt Teilhabe/Nicht-Teilhabe entsteht. Entlang der Modalitäten Verschalten, Temporalisieren und Teilhabende Kritik entwickeln die Beiträge einen differenzierten Blick auf Teilhabe im Spannungsfeld von Anspruch und Inanspruchnahme

Learning for Network Applications and Control

The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements. There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms. In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds. In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics. In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs. We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy. In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling