A Review on Features’ Robustness in High Diversity Mobile Traffic Classifications

Mobile traffics are becoming more dominant due to growing usage of mobile devices and proliferation of IoT. The influx of mobile traffics introduce some new challenges in traffic classifications; namely the diversity complexity and behavioral dynamism complexity. Existing traffic classifications methods are designed for classifying standard protocols and user applications with more deterministic behaviors in small diversity. Currently, flow statistics, payload signature and heuristic traffic attributes are some of the most effective features used to discriminate traffic classes. In this paper, we investigate the correlations of these features to the less-deterministic user application traffic classes based on corresponding classification accuracy. Then, we evaluate the impact of large-scale classification on feature's robustness based on sign of diminishing accuracy. Our experimental results consolidate the needs for unsupervised feature learning to address the dynamism of mobile application behavioral traits for accurate classification on rapidly growing mobile traffics

A Methodology for Performance Benchmarking of Mobile Networks for Internet Video Streaming

International audienceVideo streaming is a dominant contributor to the global Internet traffic. Consequently, gauging network performance w.r.t. the video Quality of Experience (QoE) is of paramount importance to both telecom operators and regulators. Modern video streaming systems, e.g. YouTube, have huge catalogs of billions of different videos that vary significantly in content type. Owing to this difference, the QoE of different videos as perceived by end users can vary for the same network Quality of Service (QoS). In this paper, we present a methodology for benchmarking performance of mobile operators w.r.t Internet video that considers this variation in QoE. We take a data-driven approach to build a predictive model using supervised machine learning (ML) that takes into account a wide range of videos and network conditions. To that end, we first build and analyze a large catalog of YouTube videos. We then propose and demonstrate a framework of controlled experimentation based on active learning to build the training data for the targeted ML model. Using this model, we then devise YouScore, an estimate of the percentage of YouTube videos that may play out smoothly under a given network condition. Finally, to demonstrate the benchmarking utility of YouScore, we apply it on an open dataset of real user mobile network measurements to compare performance of mobile operators for video streaming

Uma abordagem preditiva de DASH QoE baseada em aprendizado de máquina em multi-access edge computing

Orientador: Christian Rodolfo Esteve RothenbergDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O tráfego de serviços de vídeo multimídia está crescendo rapidamente nas redes móveis nos últimos anos. Os serviços de vídeo que usam técnicas de Dynamic Adaptive Streaming sobre HTTP (DASH) dominaram o tráfego total da Internet para transportar o tráfego de vídeo. Espera-se que as operadoras de rede móvel (Mobile Network Operators - MNOs) continuem atendendo a essa demanda crescente por tráfego de vídeo suportado por DASH, ao mesmo tempo em que fornecem uma alta qualidade de experiência (Quality of Experience - QoE) aos usuários finais. Além disso, as operadoras precisam ter um conhecimento claro acerca da qualidade de vídeo percebida pelos usuários finais e relacioná-la com o monitoramento em nível de rede, ou com informações de telemetria para identificação de problemas, análise da causa raiz e predição de padrões. Para garantir um gerenciamento de tráfego de rede com reconhecimento de QoE, um pré-requisito é que os MNOs monitorem o tráfego de rede passivamente e realizem medições efetivas de indicadores-chave de desempenho (Key Performance Indicators - KPIs) de QoE, como resoluções, eventos de paralisação, entre outros, que influenciam diretamente a percepção do usuário final. Muitas abordagens da literatura foram propostas para medir os KPIs com o objetivo de fornecer uma qualidade de serviço de vídeo aceitável. A maioria das soluções exige consciência de contexto do usuário final, o que não é viável do ponto de vista do MNO. No entanto, Deep Packet Inspection (DPI), outra solução mais amplamente usada para estimar os KPIs diretamente do tráfego de rede, não é mais uma solução conveniente para as operadoras devido à adoção de criptografia de streaming de vídeo fim-a-fim sobre TCP (HTTPs) e QUIC. Portanto, o aprendizado de máquina (Machine Learning - ML) passou a ser recentemente aceito como uma solução bem reconhecida para estimar KPIs de QoE, analisando os padrões de tráfego criptografados bem como estatísticas como qualidade de serviço (Quality of Service - QoS). Este trabalho apresenta uma abordagem mais refinada e leve, baseada em aprendizado de máquina, denominada Edge QoE Probe, para estimar QoE do usuário final para o serviço de vídeo DASH, monitorando passivamente o tráfego de rede criptografado na borda da rede. Nossa abordagem pode avaliar vários KPIs de QoE, como por exemplo resolução, taxa de bits, proporção de paralisação, entre outros, tanto em tempo real quanto por sessão. Além disso, neste trabalho investigamos o desempenho do vídeo DASH sobre o protocolo de transporte tradicional TCP (HTTPs) e QUIC. Para este propósito, avaliamos experimentalmente diferentes traces de rede celular em um ambiente emulado de alta fidelidade e comparamos o desempenho comportamental de algoritmos Adaptive Bitrate Streaming (ABS) considerando KPIs de QoE sobre TCP (HTTPs) e QUIC. Nossos resultados empíricos mostram que os algoritmos tradicionais de ABS usando QUIC como transporte precisariam alterações específicas para melhorar o desempenho em termos de QoE de vídeo baseados em DASHAbstract: Multimedia video services traffic is rapidly growing in mobile networks in recent years. Video services using Dynamic Adaptive Streaming over HTTP (DASH) techniques have dominated the total internet traffic to carry video traffic. Mobile Network Operators (MNOs) are expected to run on with this growing demand for DASH-supported video traffic while providing a high Quality of Experience (QoE) to the end-users. Besides, operators need to have a crystal notion of video quality perceived by the end-users and correlate them with network-level monitoring or telemetry information for problem identification, root cause analysis, and pattern prediction. To ensure QoE–aware network traffic management, a prerequisite for the MNOs is to monitor the network traffic passively and measure objective QoE Key Performance Indicators (KPIs) (such as resolutions and stalling events) effectively that directly influence end-user subjective feedback. Many literature approaches have been proposed to measure the KPIs aimed to deliver acceptable video service quality. Most of the solutions require end-user awareness, which is not viable from the MNOs' perspective. However, Deep Packet Inspection (DPI), another most widely used solution to estimate the KPIs directly from network traffic, is not a convenient solution anymore for the operators due to the adoption of end-to-end video streaming encryption over TCP (HTTPs) and QUIC transport protocol. Hence, in recent, Machine Learning (ML) has been accepted as a well-recognized solution for estimating QoE KPIs by analyzing the encrypted traffic patterns and statistics as Quality of Service (QoS). This work presents an ML-based lightweight and fine-grained Edge QoE Probe approach to estimate the end-user QoE for DASH video service by passively monitoring the encrypted network traffic on the edge of the network. Our approach can assess numerous QoE KPIs (such as resolution, bit-rate, quality switches, startup delay, and stall ratio) both in a real-time and per-session manner. Moreover, we investigate the DASH video service performance over the traditional TCP (HTTPs) and QUIC transport protocol in this work. For this purpose, we experimentally evaluate different cellular network traces in a high-fidelity emulated testbed and compare the behavioral performance of Adaptive Bitrate Streaming (ABS) algorithms considering QoE KPIs over TCP (HTTPs) and QUIC. Our empirical results show that QUIC suffers from traditional state-of-the-art ABS algorithms' ineffectiveness to improve video streaming performance without specific changesMestradoEngenharia de ComputaçãoMestre em Engenharia ElétricaFuncam

Improving ABR Video Streaming Design with Systematic QoE Measurement and Cross Layer Analysis

Adaptive Bitrate streaming (ABR) has been widely adopted by mobile video services to deliver satisfying Quality of Experience (QoE) over cellular network with time-varying bandwidth conditions. To build an ABR service, a wide range of critical components spanning different entities need to be determined. It is challenging to achieve designs with good QoE properties, as the streaming performance depends on complex interactions among the various factors. To make it more complex, many design decisions also involve tradeoffs among different QoE metrics. To address this challenge, in this dissertation, we build four systems to provide systematic support for video QoE measurements and cross-layer analysis. First, we build a general black-box measurement platform based on standard ABR protocols and common UI designs. It analyzes HTTP information in the network traffic and correlates UI events of mobile video apps to reveal ABR design and identify QoE issues. Second, to address the challenge brought by increasingly adopted encryption protocols such HTTPS and QUIC, we develop a technique called CSI to infer ABR video adaptation behavior based on packet size and timing information still available in the encrypted traffic. Third, we explore a conceptually very different approach to QoE measurement --- utilizing the on-device recording capability to record the video displayed on the mobile device screen and measuring delivered QoE from this recording. We design a novel system VideoEye to conduct such screen-recording-based QoE analysis. Lastly, to understand the interaction of existing video streaming system design with the new transport protocol QUIC, we build a platform WIQ to perform what-if analysis and measure the video QoE impact of QUIC without the need of modifying the server or client implementation. Leveraging these systems, we perform measurements on popular streaming services, understand the QoE implications of various ABR design, identify a wide range of QoE issues and develop best practices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155039/1/xsc_1.pd

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