Argyrios G. Tasiopoulos, Ray S. Atarashi, I. Psaras, G. Pavlou
In Shared Media Experience Services (SMESs), a group of people is interested in streaming consumption in a synchronised way, like in the case of cloud gaming, live streaming, and interactive social applications. However, group synchronisation comes at the expense of other Quality of Experience (QoE) factors due to both the dynamic and diverse network conditions that each group member experiences. Someone might wonder if there is a way to keep a group synchronised while maintaining the highest possible QoE for each one of its members. In this work, at first we create a Quality Assessment Framework capable of evaluating different SMESs improvement approaches with respect to traditional metrics like media bitrate quality, playback disruption, and end user desynchronisation. Secondly, we focus on the bitrate adaptation for improving the QoE of SMESs, as an incrementally deployable end user triggered approach, and we formulate the problem in the context of Adaptive Real Time Dynamic Programming (ARTDP). Finally, we develop and apply a simple QoE aware bitrate adaptation mechanism that we compare against youtube live-streaming traces to find that it improves the youtube performance by more than 30%.
{"title":"On the Bitrate Adaptation of Shared Media Experience Services","authors":"Argyrios G. Tasiopoulos, Ray S. Atarashi, I. Psaras, G. Pavlou","doi":"10.1145/3098603.3098608","DOIUrl":"https://doi.org/10.1145/3098603.3098608","url":null,"abstract":"In Shared Media Experience Services (SMESs), a group of people is interested in streaming consumption in a synchronised way, like in the case of cloud gaming, live streaming, and interactive social applications. However, group synchronisation comes at the expense of other Quality of Experience (QoE) factors due to both the dynamic and diverse network conditions that each group member experiences. Someone might wonder if there is a way to keep a group synchronised while maintaining the highest possible QoE for each one of its members. In this work, at first we create a Quality Assessment Framework capable of evaluating different SMESs improvement approaches with respect to traditional metrics like media bitrate quality, playback disruption, and end user desynchronisation. Secondly, we focus on the bitrate adaptation for improving the QoE of SMESs, as an incrementally deployable end user triggered approach, and we formulate the problem in the context of Adaptive Real Time Dynamic Programming (ARTDP). Finally, we develop and apply a simple QoE aware bitrate adaptation mechanism that we compare against youtube live-streaming traces to find that it improves the youtube performance by more than 30%.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127821110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Jawad Khokhar, Nawfal Abbassi Saber, Thierry Spetebroot, C. Barakat
For internet applications, measuring, modeling and predicting the quality experienced by end users as a function of network conditions is challenging. A common approach for building application specific Quality of Experience (QoE) models is to rely on controlled experimentation. For accurate QoE modeling, this approach can result in a large number of experiments to carry out because of the multiplicity of the network features, their large span (e.g., bandwidth, delay) and the time needed to setup the experiments themselves. However, most often, the space of network features in which experimentations are carried out shows a high degree of uniformity in the training labels of QoE. This uniformity, difficult to predict beforehand, amplifies the training cost with little or no improvement in QoE modeling accuracy. So, in this paper, we aim to exploit this uniformity, and propose a methodology based on active learning, to sample the experimental space intelligently, so that the training cost of experimentation is reduced. We prove the feasibility of our methodology by validating it over a particular case of YouTube streaming, where QoE is modeled both in terms of interruptions and stalling duration.
{"title":"On Active Sampling of Controlled Experiments for QoE Modeling","authors":"Muhammad Jawad Khokhar, Nawfal Abbassi Saber, Thierry Spetebroot, C. Barakat","doi":"10.1145/3098603.3098609","DOIUrl":"https://doi.org/10.1145/3098603.3098609","url":null,"abstract":"For internet applications, measuring, modeling and predicting the quality experienced by end users as a function of network conditions is challenging. A common approach for building application specific Quality of Experience (QoE) models is to rely on controlled experimentation. For accurate QoE modeling, this approach can result in a large number of experiments to carry out because of the multiplicity of the network features, their large span (e.g., bandwidth, delay) and the time needed to setup the experiments themselves. However, most often, the space of network features in which experimentations are carried out shows a high degree of uniformity in the training labels of QoE. This uniformity, difficult to predict beforehand, amplifies the training cost with little or no improvement in QoE modeling accuracy. So, in this paper, we aim to exploit this uniformity, and propose a methodology based on active learning, to sample the experimental space intelligently, so that the training cost of experimentation is reduced. We prove the feasibility of our methodology by validating it over a particular case of YouTube streaming, where QoE is modeled both in terms of interruptions and stalling duration.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122889776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kathrin Borchert, Matthias Hirth, T. Zinner, A. Göritz
Enterprise applications like SAP are part of the day-to-day work of a large number of employees. Similar to many modern applications, enterprise applications are often implemented in a distributed fashion and consequently suffer from network degradations resulting in impairments like increased loading delays. While the influence of these impairments on the perceived quality of users is well researched for consumer applications and network services, the impact of these impairments in a business environment is yet to be investigated. To address this gap we develop a non-intrusive software tool for continuously collecting subjective ratings on the performance of an enterprise application from a large number of employees. Based on the feedback from a company and results from two initial field studies we discuss the specific challenges when assessing the perceived quality of employees during regular working hours and point out our further research directions.
{"title":"Designing a Survey Tool for Monitoring Enterprise QoE","authors":"Kathrin Borchert, Matthias Hirth, T. Zinner, A. Göritz","doi":"10.1145/3098603.3098610","DOIUrl":"https://doi.org/10.1145/3098603.3098610","url":null,"abstract":"Enterprise applications like SAP are part of the day-to-day work of a large number of employees. Similar to many modern applications, enterprise applications are often implemented in a distributed fashion and consequently suffer from network degradations resulting in impairments like increased loading delays. While the influence of these impairments on the perceived quality of users is well researched for consumer applications and network services, the impact of these impairments in a business environment is yet to be investigated. To address this gap we develop a non-intrusive software tool for continuously collecting subjective ratings on the performance of an enterprise application from a large number of employees. Based on the feedback from a company and results from two initial field studies we discuss the specific challenges when assessing the perceived quality of employees during regular working hours and point out our further research directions.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126362973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Florian Wamser, Steffen Höfner, Michael Seufert, P. Tran-Gia
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.
{"title":"Server and Content Selection for MPEG DASH Video Streaming with Client Information","authors":"Florian Wamser, Steffen Höfner, Michael Seufert, P. Tran-Gia","doi":"10.1145/3098603.3098607","DOIUrl":"https://doi.org/10.1145/3098603.3098607","url":null,"abstract":"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.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121623492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the Speed of a page. In this paper we present SpeedPerception1, a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process. In Phase-1 of our SpeedPerception study using Internet Retailer Top 500 (IR 500) websites [3], we found that commonly used navigation metrics such as onLoad and Time To First Byte (TTFB) fail (less than 60% match) to represent majority human perception when comparing the speed of two webpages. We present a simple 3-variable-based machine learning model that explains the majority end-user choices better (with 87 ± 2% accuracy). In addition, our results suggest that the time needed by end-users to evaluate relative perceived speed of webpage is far less than the time of its visualComplete event.
显然，没有人喜欢低质量体验(QoE)的网页。被感知为慢或快是web应用程序整体感知QoE的关键因素。虽然在优化web应用程序方面已经投入了大量的努力(无论是在工业界还是学术界)，但在描述网页加载过程的哪些方面真正影响人类最终用户对页面速度的感知方面并没有很多工作。在本文中，我们提出了SpeedPerception1，这是一个大规模的web性能众包框架，专注于理解折叠上(ATF)网页内容的感知加载性能。我们的最终目标是创建免费的开源基准数据集，以推进人类如何感知网页加载过程的系统分析。在我们使用互联网零售商500强(IR 500)网站b[3]进行的速度感知研究的第一阶段，我们发现，在比较两个网页的速度时，常用的导航指标，如onLoad和Time To First Byte (TTFB)无法代表大多数人的感知(匹配率低于60%)。我们提出了一个简单的基于3变量的机器学习模型，可以更好地解释大多数最终用户的选择(准确率为87±2%)。此外，我们的结果表明，最终用户评估网页的相对感知速度所需的时间远远少于其visualComplete事件的时间。
{"title":"Perceived Performance of Top Retail Webpages In the Wild: Insights from Large-scale Crowdsourcing of Above-the-Fold QoE","authors":"Qingzhu Gao, Prasenjit Dey, P. Ahammad","doi":"10.1145/3098603.3098606","DOIUrl":"https://doi.org/10.1145/3098603.3098606","url":null,"abstract":"Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the Speed of a page. In this paper we present SpeedPerception1, a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process. In Phase-1 of our SpeedPerception study using Internet Retailer Top 500 (IR 500) websites [3], we found that commonly used navigation metrics such as onLoad and Time To First Byte (TTFB) fail (less than 60% match) to represent majority human perception when comparing the speed of two webpages. We present a simple 3-variable-based machine learning model that explains the majority end-user choices better (with 87 ± 2% accuracy). In addition, our results suggest that the time needed by end-users to evaluate relative perceived speed of webpage is far less than the time of its visualComplete event.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132659978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding the quality of web browsing enjoyed by users is key to optimize services and keep users' loyalty. This is crucial for Internet Service Providers (ISPs) to anticipate problems. Quality is subjective, and the complexity of today's pages challenges its measurement. OnLoad time and SpeedIndex are notable attempts to quantify web performance. However, these metrics are computed using browser instrumentation and, thus, are not available to ISPs. PAIN (PAssive INdicator) is an automatic system to observe the performance of web pages at ISPs. It leverages passive flow-level and DNS measurements which are still available in the network despite the deployment of HTTPS. With unsupervised learning, PAIN automatically creates a model from the timeline of requests issued by browsers to render web pages, and uses it to analyze the web performance in real-time. We compare PAIN to indicators based on in-browser instrumentation and find strong correlations between the approaches. It reflects worsening network conditions and provides visibility into web performance for ISPs.
{"title":"PAIN: A Passive Web Speed Indicator for ISPs","authors":"Martino Trevisan, I. Drago, M. Mellia","doi":"10.1145/3098603.3098605","DOIUrl":"https://doi.org/10.1145/3098603.3098605","url":null,"abstract":"Understanding the quality of web browsing enjoyed by users is key to optimize services and keep users' loyalty. This is crucial for Internet Service Providers (ISPs) to anticipate problems. Quality is subjective, and the complexity of today's pages challenges its measurement. OnLoad time and SpeedIndex are notable attempts to quantify web performance. However, these metrics are computed using browser instrumentation and, thus, are not available to ISPs. PAIN (PAssive INdicator) is an automatic system to observe the performance of web pages at ISPs. It leverages passive flow-level and DNS measurements which are still available in the network despite the deployment of HTTPS. With unsupervised learning, PAIN automatically creates a model from the timeline of requests issued by browsers to render web pages, and uses it to analyze the web performance in real-time. We compare PAIN to indicators based on in-browser instrumentation and find strong correlations between the approaches. It reflects worsening network conditions and provides visibility into web performance for ISPs.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126963268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
HTTP/2 was recently standardized to optimize the Web by promising faster Page Load Times (PLT) as compared to the widely deployed HTTP/1.1. One promising feature is HTTP/2 server push, which turns the former pull-only into a push-enabled Web. By enabling servers to preemptively push resources to the clients without explicit request, it promises further improvements of the overall PLT. Despite this potential, it remains unknown if server push can indeed yield human perceivable improvements. In this paper, we address this open question by assessing server push in both i) a laboratory and ii) a crowdsourcing study. Our study assesses the question if server push can lead to perceivable faster PLTs as compared to HTTP/1.1 and HTTP/2 without push. We base this study on a set of 28 push-enabled real-word websites selected in an Internet-wide measurement. Our results reveal that our subjects are able to perceive utilization of server push. However, its usage does not necessarily accomplish perceived PLT improvements and can sometimes even be noticeably detrimental.
{"title":"A QoE Perspective on HTTP/2 Server Push","authors":"T. Zimmermann, Benedikt Wolters, O. Hohlfeld","doi":"10.1145/3098603.3098604","DOIUrl":"https://doi.org/10.1145/3098603.3098604","url":null,"abstract":"HTTP/2 was recently standardized to optimize the Web by promising faster Page Load Times (PLT) as compared to the widely deployed HTTP/1.1. One promising feature is HTTP/2 server push, which turns the former pull-only into a push-enabled Web. By enabling servers to preemptively push resources to the clients without explicit request, it promises further improvements of the overall PLT. Despite this potential, it remains unknown if server push can indeed yield human perceivable improvements. In this paper, we address this open question by assessing server push in both i) a laboratory and ii) a crowdsourcing study. Our study assesses the question if server push can lead to perceivable faster PLTs as compared to HTTP/1.1 and HTTP/2 without push. We base this study on a set of 28 push-enabled real-word websites selected in an Internet-wide measurement. Our results reveal that our subjects are able to perceive utilization of server push. However, its usage does not necessarily accomplish perceived PLT improvements and can sometimes even be noticeably detrimental.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130105229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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