Demonstrating the prospects of dynamic application-aware networking in a home environment

Wamser, Florian; Zinner, Thomas; Iffländer, Lukas; Tran-Gia, Phuoc

doi:10.1145/2619239.2631450

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…To measure the video QoE, Aggarwal et al [4] introduces an approach they call Prometheus to estimate the QoE of mobile applications based on a combination of passive in-network measurements and client-side measurements; machine learning (ML) techniques were adopted to map the video QoS to QoE. Wamser et al [13] present a realtime QoE assessment framework called QoM for video services. The influence factors include KPIs of QoE, network conditions and application-level parameters.…”

Section: Related Workmentioning

confidence: 99%

QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities

Pan

Cheng

2018

IEEE Access

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Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. INDEX TERMSHyper text transfer protocol over secure socket layer (HTTPS) YouTube, QoE assessment, adaptive streaming, machine learning, smart city.

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Section: Related Workmentioning

confidence: 99%

QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities

Pan

Cheng

2018

IEEE Access

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“…Moreover, the recent trend towards HTTPS (e.g., YouTube, Vimeo) is about to inhibit its applicability. Clientside measurements (e.g., YoMo [3], [4]), on the other hand, are more extensive as end users are directly involved, but they can provide an individual and accurate view on objective key performance indicators (KPIs). Those KPIs provide more elaborate information about the perceived service quality and can be mapped to QoE by means of QoE models [5].…”

Section: Introductionmentioning

confidence: 99%

YoMoApp: A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks

Wamser

Seufert

Casas

et al. 2015

2015 European Conference on Networks and Communications (EuCNC)

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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.

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“…In this way, the utility per allocated network resources in terms of QoE can directly be influenced. The quality indicators can be fetched from the network based on in‐network measurements . A typical implementation of the concept consists of three entities: (i) the network and application monitoring deriving the key performance indicators and network characteristics ; (ii) a decision unit ; and (iii) a resource management action that changes the traffic handling within the network (e.g.…”

Section: Introductionmentioning

confidence: 99%

Modelling and performance analysis of application‐aware resource management

et al. 2015

Self Cite

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Summary Application‐aware resource management is the approach to tailor access networks to have characteristics beneficial for the running applications and services. This is achieved through the monitoring and integration of key performance indicators from the application layer within the network resource management. The aim is to increase user‐perceived quality and network resource efficiency by traffic engineering with the help of these indicators. Using analytic and simulative approaches, this paper provides analysis methods for network operators to quantify the performance gains of alternative resource allocation algorithms that implement the application‐aware concept. Network operators can use the proposed methods to evaluate possible performance gain trade‐offs between investing in a pure capacity increase (over‐provisioning) and the realization of an application‐aware resource allocation. For this purpose, we model and analyse the application quality trade‐offs of four algorithms for application‐aware resource management at a single link in varying traffic situations. The algorithms are chosen with respect to different complexity and implementation level in order to cover the design space in a systematic way. The study of the algorithms focuses on the application‐layer performance for the most used applications today, namely, web browsing and video streaming with constant bit rate as well as hypertext transfer protocol progressive streaming with variable bit rate. Application quality trade‐offs are analysed in particular for a high‐resource utilization at a bottleneck link. The results confirm that application‐aware resource management outperforms best‐effort resource management in terms of quality of experience. Moreover, our study provides guidelines for the selection and configuration of the evaluated algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

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Demonstrating the prospects of dynamic application-aware networking in a home environment

Cited by 7 publications

References 2 publications

QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities

QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities

YoMoApp: A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks

Modelling and performance analysis of application‐aware resource management

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