Detecting Degradation of Web Browsing Quality of Experience

This dataset represents 222k samples of web browsing session measurements collected during 2.5 months using the Web View platform (https://webview.orange.com) [1]. Web View allows different probes to automatically execute multiple web sessions in a real end-user environment. In our test campaign, we use 17 machines, spread in three different locations worldwide (Lannion, Paris and Mauritius islands), different ISPs and access technologies (ADSL, WiFi and fiber) for a total of 9 combinations, and up to 12 browser versions, which include various versions of Chrome and Firefox. Each machine can request a different browser viewport, can enable or disable the AdBlock plugin to emulate different user preferences and can request a specific network protocol (HTTP/1, HTTP/2 or QUIC).We leverage this dataset to phrase the QoE degradation detection issue as a change point detection problem in [2]. Our results, beyond showing feasibility, warn about the exclusive use of QoE indicators that are very close to content, as changes in the content space can lead to false alarms that are not tied to network-related problems.If you use these datasets in your research, you can reference the appropriate papers:[1] A. Saverimoutou, B. Mathieu, and S. Vaton, “Web View: A measurement platform for depicting web browsing performance and delivery,” IEEE Communications Magazine, vol. 58, no. 3, pp. 33–39, 2020.[2] A. Huet, Z. Ben Houidi, B. Mathieu, D. Rossi “Detecting degradation of web browsing quality of experience,” 16th International Conference on Network and Service Management (CNSM), 2020.Each row represents one experiment, and the columns are as follows:- wwwName: Target page- timestamp: Timestamp with format YYYY-MM-DD hh:mm:ss- browserUsed: Internet browser and version - requestedProtocol: Requested L7 protocol- adBlocker: Whether adBlocker is used or not- networkIface: Network interface- winSize: Window size- visiblePortion: Visible portion of the page that is above the fold in percents- h1Share: Share of the traffic coming from HTTP/1 in percents- h2Share: Share of the traffic coming from HTTP/2 in percents- hqShare: Share of the traffic coming from QUIC in percents- pushShare: Share of the traffic coming from HTTP/2 Server Push in percents- nbRes: Number of objects of the page- nbResNA: Number of objects coming from North America- nbResSA: Number of objects coming from South America- nbResEU: Number of objects coming from Europe- nbResAS: Number of objects coming from Asia- nbResAF: Number of objects coming from Africa- nbResOC: Number of objects coming from Oceania- nbResUKN: Number of objects coming from unknown provenance- nbHTTPS: Number of objects coming from an HTTPS connection- nbHTTP: Number of objects coming from an HTTP connection- nbDomNA: Number of different domain names coming from North America- nbDomSA: Number of different domain names coming from South America- nbDomEU: Number of different domain names coming from Europe- nbDomAS: Number of different domain names coming from Asia- nbDomAF: Number of different domain names coming from Africa- nbDomOC: Number of different domain names coming from Oceania- firstPaint: First paint time (ms)- tfvr: Time for Full Visual Rendering (ms)- dom: DOM time (ms)- plt: Page Load Time (ms)- machine: Machine name (containing location information)- categoryType: Category of the web page- pageSize: Total web page size (bytes)- receiveTime: Total receive time from HAR (ms)- transferRate: Transfer rate (bps)- id: Unique identification of the current experiment- config: Identification for the tuple (browserUsed, requestedProtocol, adBlocker, networkIface, winSize, machine, wwwName), i.e. the probe configuration with target wwwName

本数据集汇聚了在2.5个月的时间内，通过Web View平台（https://webview.orange.com）[1]收集的22.2万次网络浏览会话测量样本。Web View平台允许多种探测器在真实终端用户环境中自动执行多个网络会话。在我们的测试活动中，我们运用了分布在三个不同地理位置（兰尼翁、巴黎和毛里求斯群岛）的17台机器，涵盖了不同的互联网服务提供商（ISP）和接入技术（ADSL、WiFi和光纤），共计9种组合，以及多达12个浏览器版本，包括不同版本的Chrome和Firefox。每台机器均可请求不同的浏览器视口，可启用或禁用AdBlock插件以模拟不同的用户偏好，并可请求特定的网络协议（HTTP/1、HTTP/2或QUIC）。我们利用此数据集将网络浏览质量体验下降检测问题表述为[2]中的变化点检测问题。我们的研究成果不仅证明了可行性，还警示了仅使用与内容极为接近的QoE指标可能带来的风险，因为内容空间的变化可能导致与网络相关问题无关的误报。若您在研究中使用这些数据集，请引用相应的论文：[1] A. Saverimoutou, B. Mathieu, 和 S. Vaton，“Web View：一种描绘网络浏览性能和交付的测量平台”，IEEE通信杂志，第58卷，第3期，第33–39页，2020。[2] A. Huet, Z. Ben Houidi, B. Mathieu, 和 D. Rossi，“检测网络浏览质量体验的下降”，第16届网络和服务管理国际会议（CNSM），2020。每一行代表一次实验，各列内容如下：- wwwName：目标页面- timestamp：时间戳，格式为YYYY-MM-DD hh:mm:ss- browserUsed：使用的互联网浏览器及版本- requestedProtocol：请求的第七层协议- adBlocker：是否使用AdBlock插件- networkIface：网络接口- winSize：窗口大小- visiblePortion：页面可折叠部分的可见百分比- h1Share：来自HTTP/1的流量占比（百分比）- h2Share：来自HTTP/2的流量占比（百分比）- hqShare：来自QUIC的流量占比（百分比）- pushShare：来自HTTP/2服务器推送的流量占比（百分比）- nbRes：页面对象数量- nbResNA：来自北美的对象数量- nbResSA：来自南美的对象数量- nbResEU：来自欧洲的对象数量- nbResAS：来自亚洲的对象数量- nbResAF：来自非洲的对象数量- nbResOC：来自大洋洲的对象数量- nbResUKN：来自未知来源的对象数量- nbHTTPS：来自HTTPS连接的对象数量- nbHTTP：来自HTTP连接的对象数量- nbDomNA：来自北美的不同域名数量- nbDomSA：来自南美的不同域名数量- nbDomEU：来自欧洲的不同域名数量- nbDomAS：来自亚洲的不同域名数量- nbDomAF：来自非洲的不同域名数量- nbDomOC：来自大洋洲的不同域名数量- firstPaint：首次绘制时间（毫秒）- tfvr：完整视觉渲染时间（毫秒）- dom：DOM时间（毫秒）- plt：页面加载时间（毫秒）- machine：机器名称（包含位置信息）- categoryType：网页类别- pageSize：网页总大小（字节）- receiveTime：从HAR接收的总时间（毫秒）- transferRate：传输速率（bps）- id：当前实验的唯一标识- config：标识（browserUsed, requestedProtocol, adBlocker, networkIface, winSize, machine, wwwName）的元组，即带有目标wwwName的探测器配置。