Between July and September 2022, BYJU's emerged as the top Ed Tech platform for K12 and test preparation In India. It recorded approximately *** million website visits. Following closely behind was Toppr.com, with around *** million visits during the same period.

The global website visitor tracking software market is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of digital marketing strategies, the growing importance of data-driven decision-making, and the increasing sophistication of website visitor tracking tools. Cloud-based solutions dominate the market due to their scalability, accessibility, and cost-effectiveness, particularly appealing to Small and Medium-sized Enterprises (SMEs). However, large enterprises continue to invest significantly in on-premise solutions for enhanced data security and control. The market is highly competitive, with numerous established players and emerging startups offering a range of features and functionalities. Technological advancements, such as AI-powered analytics and enhanced integration with other marketing tools, are shaping the future of the market. The market's geographical distribution reflects the global digital landscape. North America, with its mature digital economy and high adoption rates, holds a significant market share. However, regions like Asia-Pacific are showing rapid growth, driven by increasing internet penetration and digitalization across various industries. Despite the overall positive outlook, challenges such as data privacy regulations and the increasing complexity of website tracking technology are influencing market dynamics. The ongoing competition among vendors necessitates continuous innovation and the development of more user-friendly and insightful tools. The future growth of the website visitor tracking software market is promising, fueled by the continuing importance of data-driven decision-making within marketing and business strategies. A key factor will be the ongoing adaptation to evolving privacy regulations and user expectations.

Be ready for a cookieless internet while capturing anonymous website traffic data!

By installing the resolve pixel onto your website, business owners can start to put a name to the activity seen in analytics sources (i.e. GA4). With capture/resolve, you can identify up to 40% or more of your website traffic. Reach customers BEFORE they are ready to reveal themselves to you and customize messaging toward the right product or service.

This product will include Anonymous IP Data and Web Traffic Data for B2B2C.

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Super easy to implement and extraordinarily fast at processing, business owners are thrilled with the enhanced identity resolution capabilities powered by VisitIQ's First Party Opt-In Identity Platform. Capture/resolve and identify your Ideal Customer Profiles to customize marketing. Identify WHO is looking, WHAT they are looking at, WHERE they are located and HOW the web traffic came to your site.

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Attributes of sites in Hamilton City which collect anonymised data from a sample of vehicles. Note: A Link is the section of the road between two sites

Column_InfoSite_Id, int : Unique identiferNumber, int : Asset number. Note: If the site is at a signalised intersection, Number will match 'Site_Number' in the table 'Traffic Signal Site Location'Is_Enabled, varchar : Site is currently enabledDisabled_Date, datetime : If currently disabled, the date at which the site was disabledSite_Name, varchar : Description of the site locationLatitude, numeric : North-south geographic coordinatesLongitude, numeric : East-west geographic coordinates

Relationship









Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Only Test Site Korean Traffic Light 2

## Overview

Only Test Site Korean Traffic Light 2 is a dataset for object detection tasks - it contains Green Red Left PZAm annotations for 2,038 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Web Performance Testing Market size was valued at USD 3.22 Billion in 2024 and is projected to reach USD 8.14 Billion by 2031, growing at a CAGR of 8.72% during the forecast period 2024-2031.

Global Web Performance Testing Market Drivers

The market drivers for the Web Performance Testing Market can be influenced by various factors. These may include:

Increased Internet and Mobile Usage: The proliferation of internet users globally and the surge in mobile device usage means that more people are accessing websites and web applications through various devices and networks. This diversity necessitates robust performance testing to ensure a consistent user experience across all platforms. Rising E-commerce and Online Services: The growth of e-commerce and online services demands that websites perform optimally at all times. Slow load times or downtime can directly translate to lost revenue and poor customer satisfaction. Hence, businesses must continuously test and optimize their web performance to remain competitive. User Experience (UX) Focus: Companies are increasingly prioritizing user experience as a key differentiator. A smooth, responsive, and fast website is essential for retaining visitors and enhancing user satisfaction. Performance testing helps in identifying and resolving issues that could hinder UX. SEO and Digital Marketing: Search engines like Google consider page load times and overall web performance as critical factors in their ranking algorithms. Websites that load faster are more likely to rank higher, driving organic traffic and improving visibility. Performance testing ensures websites meet these criteria. Complex Web Applications: Modern web applications often involve complex interactions, real-time updates, and integrations with other services. Ensuring these applications function correctly under various conditions requires comprehensive performance testing. Regulatory Requirements: Several industries are subject to regulatory requirements that mandate specific performance standards for websites, particularly for accessibility and user data protection. Compliance necessitates regular performance testing. Competitive Pressure: In a crowded digital marketplace, even minor performance improvements can provide a competitive edge. Companies invest in performance testing to stay ahead of or keep up with competitors. Technology Advancements: Innovations in web technologies (like Progressive Web Apps, Single Page Applications, etc.) necessitate new and improved testing methodologies. The adoption of these new technologies requires businesses to adapt and enhance their performance testing strategies. Cloud Adoption: The increasing use of cloud services and decentralized architectures facilitates scalability but also introduces performance variability. Cloud-based web performance testing tools are essential to monitor and optimize performance in such environments. Customer Expectations: The modern consumer expects instantaneous access to information and services. Even slight delays can result in frustration and abandonment. Meeting these heightened customer expectations is a major driver for continuous web performance testing. A/B Testing and Optimization: Businesses frequently use A/B testing to optimize website elements for conversion improvements. Performance testing ensures that these optimizations do not negatively impact load times or responsiveness. Emerging Markets and Global Reach: As businesses expand globally, they must ensure their websites perform well across different geographies, which may have varied network conditions and device usage patterns. Performance testing is crucial for maintaining a global online presence.

The global website speed and performance test tool market size was valued at USD 1.84 billion in 2022 and is projected to reach USD 5.52 billion by 2033, exhibiting a CAGR of 12.0% during the forecast period. The escalating demand for website performance optimization services, the surge in website traffic, and the proliferation of mobile devices drive market growth. Moreover, the growing adoption of cloud-based solutions and the increasing preference for online shopping fuel market expansion. Key players in the website speed and performance test tool market include Pingdom, Yellow Lab Tools, Alerta, Sematext, Domsignal, Dareboost, New Relic, Google PageSpeed Insights, KeyCDN Website Speed Test, Yslow, Uptrends, GTmetrix, Site24x7, Datadog, Catchpoint WebPageTest, Dotcom-Monitor, Lighthouse, WebPagetest, and Load Impact. These companies are focusing on offering advanced features and enhancing the capabilities of their tools to gain a competitive edge. The market is fragmented, with several players offering a wide range of solutions catering to different customer needs and industries.

Load Testing Tools Market Outlook

The global load testing tools market size was valued at approximately USD 1.5 billion in 2023 and is anticipated to reach USD 3.2 billion by 2032, witnessing a robust compound annual growth rate (CAGR) of 8.9% during the forecast period. This growth is primarily driven by the increasing demand for efficient software solutions that ensure optimal performance and reliability of applications across various industries. As businesses continue to embrace digital transformation, the reliance on software applications has grown exponentially, necessitating the need for load testing tools to guarantee performance under varying conditions.

One of the primary growth factors for the load testing tools market is the rapid expansion of the IT and telecommunications industry. With the advent of 5G technology and the increasing proliferation of connected devices, there is a heightened need to test the performance and scalability of applications, networks, and infrastructure. This has significantly boosted the demand for advanced load testing tools that can simulate real-world traffic and provide actionable insights into system performance. Additionally, the shift towards cloud-based solutions has further fueled demand, as organizations look to ensure seamless user experiences across platforms.

Another key driver for market growth is the increasing adoption of agile and DevOps methodologies in software development. These methodologies emphasize a continuous integration and continuous delivery (CI/CD) approach, where load testing is an integral part of the development process to identify bottlenecks and optimize performance. As organizations strive for faster time-to-market and improved software quality, the integration of load testing tools into their development pipeline has become a necessity. The tools not only facilitate early detection of performance issues but also enable developers to fine-tune applications for optimal scalability.

The rise of e-commerce and digital retail has also contributed significantly to the growth of the load testing tools market. As online retail platforms continue to evolve and attract larger traffic volumes, ensuring system reliability and performance during peak loads has become critical. Load testing tools play a vital role in simulating user traffic and identifying potential vulnerabilities that could impact user experience and business operations. This trend is particularly evident during seasonal peaks, such as Black Friday or Cyber Monday, where load testing ensures that websites can handle increased traffic without compromising on performance.

In the context of software development, the role of Functional and Testing Tools cannot be overstated. These tools are essential for ensuring that applications not only meet functional requirements but also perform optimally under load conditions. By integrating functional testing with load testing, developers can identify potential issues early in the development cycle, thereby reducing the risk of performance bottlenecks. This approach is particularly beneficial in agile and DevOps environments, where continuous integration and delivery are key. The synergy between functional and load testing tools enables a comprehensive evaluation of an application's performance, ensuring that it can handle real-world usage scenarios effectively.

From a regional perspective, North America currently dominates the load testing tools market, attributed to the presence of major technology firms and a highly developed IT infrastructure. The region's strong emphasis on technological innovation and digital transformation initiatives has spurred demand for advanced testing solutions. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The burgeoning IT sector, along with rapid digitalization across industries in countries like China and India, is driving the demand for load testing tools. Furthermore, government initiatives supporting digital transformation and the adoption of cloud technologies are further propelling market growth in the region.

Component Analysis

The load testing tools market is segmented into software and services components, each playing a vital role in ensuring robust application performance. The software segment encompasses a wide range of tools designed to simulate user traffic and analyze system behavior under load conditions. These tools are increasingly incorporat

The global Web Application Performance Testing Tool market is anticipated to grow exponentially over the forecast period. In 2025, the market was valued at approximately million USD and is projected to reach over million USD by 2033. The expanding need for efficient web applications to enhance user experience and the increasing adoption of cloud-based testing solutions are key drivers propelling market growth. Additionally, the surge in digital transformation initiatives across various industries is further contributing to the market's expansion. The market is segmented based on type, application, and region. Load testing tools hold a substantial market share due to their ability to simulate real-world user traffic and identify performance bottlenecks. The enterprise application segment dominates the market, driven by the growing adoption of web applications in enterprise environments. Regionally, North America accounts for a significant market share due to the presence of leading technology companies and the early adoption of advanced testing tools. Asia Pacific is expected to witness the highest growth rate over the forecast period, attributed to the increasing adoption of web applications and the growing number of technology startups in the region.

Road Test Locations for all DMV Road Test Types mandated by NYS Vehicle and Traffic Law.

Code:

• Packet_Features_Generator.py & Features.py
  • To run this code:
    • pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j
    • -h, --help show this help message and exit
      -i TXTFILE input text file
      -x X Add first X number of total packets as features.
      -y Y Add first Y number of negative packets as features.
      -z Z Add first Z number of positive packets as features.
      -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,...
      -s S Generate samples using size s.
      -j
  • Purpose:
    • Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.
    • Uses Features.py to calcualte the features.
• startMachineLearning.sh & machineLearning.py
  • To run this code:
    • bash startMachineLearning.sh
    • This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags
    • Options (to be edited within this file):
      • --evaluate-only to test 5 fold cross validation accuracy
      • --test-scaling-normalization to test 6 different combinations of scalers and normalizers
        • Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use
      • --grid-search to test the best grid search hyperparameters
        - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:'
        - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'
  • Purpose:
    • Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

• Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.
• Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:
  • First number is a classification number to denote what website, query, or vr action is taking place.
  • The remaining numbers in each line denote:
    • The size of a packet,
    • and the direction it is traveling.
  • negative numbers denote incoming packets
  • positive numbers denote outgoing packets

Web Performance Testing Market Outlook

The global web performance testing market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 2.9 billion by 2032, growing at a robust CAGR of 10.2% from 2024 to 2032. The substantial growth in this market is primarily driven by the increasing demand for seamless user experiences, enhanced digital transformation across various industries, and the proliferation of web applications.

One of the primary growth factors for the web performance testing market is the exponential increase in internet users and the consequent rise in web traffic. With more users accessing services online, companies are compelled to optimize their web applications to ensure high performance and reliability. This surge in web traffic necessitates rigorous performance testing to mitigate any risks of downtime or sluggishness, thereby boosting the demand for web performance testing solutions.

Another significant factor contributing to market growth is the growing complexity of web applications. Modern web applications are often feature-rich and require integration with various third-party services, making them more susceptible to performance issues. This complexity drives organizations to adopt sophisticated web performance testing tools that can simulate real-world user scenarios and identify potential bottlenecks before they impact end users. As a result, the demand for comprehensive testing solutions has seen a notable rise.

The increasing adoption of cloud computing and DevOps practices is also a key driver of market growth. Cloud-based infrastructure and DevOps methodologies enable faster development cycles and continuous integration/continuous deployment (CI/CD) pipelines, necessitating continuous testing to ensure application stability and performance. Web performance testing solutions that are integrated with CI/CD pipelines are becoming indispensable for organizations aiming to deliver high-quality software at a rapid pace.

From a regional perspective, North America holds a significant share of the web performance testing market, driven by the high adoption rate of advanced technologies and the presence of major industry players. However, the Asia-Pacific region is anticipated to witness the highest growth rate during the forecast period. This growth is attributed to the rapid digitization, increasing internet penetration, and rising investments in IT infrastructure across emerging economies like India and China.

Component Analysis

The web performance testing market by component is bifurcated into software and services. Software solutions dominate this segment, accounting for the largest market share. This is due to the increasing need for automated tools that can efficiently perform various types of web performance tests, such as load, stress, and capacity testing. These software solutions are crucial for identifying performance issues before they affect end-users, thereby enabling organizations to maintain high standards of web application performance.

The services segment, although smaller in comparison, is experiencing significant growth. This includes consulting, implementation, and support services that help organizations to effectively deploy and manage web performance testing tools. As web applications become more complex, the demand for specialized expertise to handle performance testing increases, driving the growth of the services segment. Additionally, the trend towards outsourcing performance testing tasks to third-party service providers is gaining traction, contributing further to this segment's expansion.

In this evolving landscape, Website Performance Monitoring Software plays a crucial role in ensuring that web applications meet the high expectations of users. These software solutions provide organizations with the tools needed to track and analyze the performance of their websites in real-time, identifying potential issues before they affect the end-user experience. By leveraging advanced analytics and monitoring capabilities, businesses can gain valuable insights into their web performance, enabling them to make data-driven decisions to enhance user satisfaction. As the demand for seamless online experiences continues to grow, the adoption of Website Performance Monitoring Software is becoming increasingly essential for organizations looking to maintain a competitive edge in the digital marketplac

Load Testing Service Market Outlook

The global Load Testing Service Market size was valued at approximately USD 1.5 billion in 2023 and is expected to grow at a compound annual growth rate (CAGR) of 15% from 2024 to 2032, reaching an estimated USD 5.2 billion by 2032. This rapid growth is driven by the escalating demand for high-performance applications across various sectors, as businesses increasingly rely on digital platforms for operations, customer engagement, and service delivery.

The surge in digital transformation initiatives, accelerated by the COVID-19 pandemic, has placed an unprecedented emphasis on the performance and reliability of web applications and services. Organizations are now more focused than ever on ensuring their digital infrastructure can handle peak loads and provide seamless user experiences. This has consequently driven the demand for load testing services, which help businesses identify performance bottlenecks and optimize their applications to handle increased user traffic efficiently. The growing adoption of cloud computing, which enables scalable and flexible testing environments, is another significant factor contributing to the market's growth.

Furthermore, the proliferation of e-commerce and online retail platforms has created immense pressure on these businesses to ensure their websites and applications can manage high volumes of traffic, especially during peak shopping seasons. Load testing services are crucial in this context, as they help e-commerce companies prevent website crashes and ensure a smooth shopping experience for customers. This growing reliance on digital sales channels is expected to continue driving the demand for load testing services in the coming years.

In addition, the increasing complexity of IT infrastructure and applications, driven by the adoption of microservices architecture, containers, and DevOps practices, has necessitated more robust and comprehensive load testing solutions. Organizations are seeking advanced load testing services that can simulate real-world traffic scenarios and provide actionable insights into application performance under various load conditions. This trend is expected to further fuel the growth of the load testing service market over the forecast period.

Performance Testing Software plays a pivotal role in the realm of load testing services. As businesses strive to ensure their applications can handle increased traffic and deliver seamless user experiences, the demand for sophisticated performance testing software has surged. These tools are designed to simulate real-world user interactions and stress-test applications under various conditions, providing critical insights into potential bottlenecks and areas for optimization. With the integration of AI and ML technologies, performance testing software is becoming more adept at predicting and mitigating performance issues before they impact end users. This evolution in software capabilities is driving its adoption across industries, as organizations seek to enhance their digital platforms' reliability and efficiency.

Regionally, North America is expected to dominate the load testing service market, owing to the high concentration of technology companies, early adoption of advanced IT solutions, and a strong focus on digital transformation across various industries. Europe and Asia Pacific are also anticipated to witness significant growth, driven by the increasing adoption of cloud-based solutions, expanding e-commerce sector, and rising awareness about the importance of application performance testing.

Component Analysis

The load testing service market is segmented into two primary components: software and services. The software segment includes various load testing tools and platforms that enable organizations to simulate user traffic and identify performance issues in their applications. These tools can be deployed on-premises or accessed via the cloud, offering flexibility and scalability to meet the diverse needs of businesses. The growing complexity of applications and the need for continuous performance monitoring have driven the demand for advanced load testing software solutions. These solutions offer features such as real-time analytics, detailed performance reports, and automated testing capabilities, making them essential for modern IT environments.

On the other hand, the services segment encompasses a range of professional servi

While it would be nice to have an endless stream of traffic to your site, for many brands it’s more of a trickle–and, in some cases, a very, very small trickle. You may not be able to generate thousands of visitors overnight, so it’s difficult to run quick conversion optimization tests to improve your sales. […]

Ever since eCommerce became a mainstream business, website owners have wrestled with the thorny problem of getting more website sales from the same amount of website traffic. There are many ways to do this but what if there was an easy way to stop your website visitors from leaving your site so quickly? There are […]

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

The cloud load testing market is experiencing robust growth, driven by the increasing adoption of cloud-based applications and the need for ensuring their scalability and performance under heavy load. The market, estimated at $5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching a substantial market size. This expansion is fueled by several key factors. Firstly, the rising complexity of modern applications, incorporating microservices and APIs, necessitates sophisticated load testing solutions to identify and resolve performance bottlenecks before deployment. Secondly, the shift towards DevOps and Agile methodologies necessitates frequent testing cycles, demanding efficient and scalable cloud-based load testing tools. Finally, the growing emphasis on digital transformation and the increasing reliance on e-commerce platforms are pushing businesses to ensure their applications can handle peak traffic and maintain optimal performance, thereby driving demand for cloud load testing services. The market is segmented by deployment type (public and private cloud) and user type (large enterprises and SMEs). Large enterprises currently dominate the market due to their greater IT budgets and complex application landscapes. However, SMEs are increasingly adopting cloud load testing solutions as they recognize the importance of application performance for their business success. Geographically, North America and Europe currently hold significant market share, but the Asia-Pacific region is exhibiting rapid growth due to its burgeoning digital economy and increasing adoption of cloud technologies. Competitive landscape is characterized by a mix of established players like Broadcom, Akamai, Google Cloud, AWS, and Microsoft Azure, alongside specialized cloud load testing vendors like LoadView, K6, and Tricentis. The future of the market will likely see increased integration with other DevOps tools, enhanced AI-powered analytics for test result interpretation, and a continued rise in demand for serverless and containerized load testing solutions.

We present a dataset targeting a large set of popular pages (Alexa top-500), from probes from several ISPs networks, browsers software (Chrome, Firefox) and viewport combinations, for over 200,000 experiments realized in 2019.We purposely collect two distinct sets with two different tools, namely Web Page Test (WPT) and Web View (WV), varying a number of relevant parameters and conditions, for a total of 200K+ web sessions, roughly equally split among WV and WPT. Our dataset comprises variations in terms of geographical coverage, scale, diversity and representativeness (location, targets, protocol, browser, viewports, metrics).For Web Page Test, we used the online service www.webpagetest.org at different locations worldwide (Europe, Asia, USA) and private WPT instances in three locations in China (Beijing, Shanghai, Dongguan). The list of target URLs comprised the main pages and five random subpages from Alexa top-500 worldwide and China. We varied network conditions : native connections and 4G, FIOS, 3GFast, DSL, and custom shaping/loss conditions. The other elements in the configuration were fixed: Chrome browser on desktop with a fixed screen resolution, HTTP/2 protocol and IPv4.For Web View, we collected experiments from three machines located in France. We selected two versions of two browser families (Chrome 75/77, Firefox 63/68), two screen sizes (1920x1080, 1440x900), and employ different browser configurations (one half of the experiments activate the AdBlock plugin) from two different access technologies (fiber and ADSL). From a protocol standpoint, we used both IPv4 and IPv6, with HTTP/2 and QUIC, and performed repeated experiments with cached objects/DNS. Given the settings diversity, we restricted the number of websites to about 50 among the Alexa top-500 websites, to ensure statistical relevance of the collected samples for each page.The two archives IFIPNetworking2020_WebViewOrange.zip and IFIPNetworking2020_Webpagetest.zip correspond respectively to the Web View experiments and to the Web Page Test experiments.Each archive contains three files:- config.csv: Description of parameters and conditions for each run,- metrics.csv: Value of different metrics collected by the browser,- progressionCurves.csv: Progression curves of the bytes progress as seen by the network, from 0 to 10 seconds by steps of 100 milliseconds,- listUrl folder: Indexes the sets of urls.Regarding config.csv, the columns are: - index: Index for this set of conditions, - location: Location of the machine, - listUrl: List of urls, located in the folder listUrl - browserUsed: Internet browser and version - terminal: Desktop or Mobile - collectionEnvironment: Identification of the collection environment - networkConditionsTrafficShaping (WPT only): Whether native condition or traffic shaping (4G, FIOS, 3GFast, DSL, or custom Emulator conditions) - networkConditionsBandwidth (WPT only): Bandwidth of the network - networkConditionsDelay (WPT only): Delay in the network - networkConditions (WV only): network conditions - ipMode (WV only): requested L3 protocol, - requestedProtocol (WV only): requested L7 protocol - adBlocker (WV only): Whether adBlocker is used or not - winSize (WV only): Window sizeRegarding metrics.csv, the columns are: - id: Unique identification of an experiment (consisting of an index 'set of conditions' and an index 'current page') - DOM Content Loaded Event End (ms): DOM time, - First Paint (ms) (WV only): First paint time, - Load Event End (ms): Page Load Time from W3C, - RUM Speed Index (ms) (WV only): RUM Speed Index, - Speed Index (ms) (WPT only): Speed Index, - Time for Full Visual Rendering (ms) (WV only): Time for Full Visual Rendering - Visible portion (%) (WV only): Visible portion, - Time to First Byte (ms) (WPT only): Time to First Byte, - Visually Complete (ms) (WPT only): Visually Complete used to compute the Speed Index, - aatf: aatf using ATF-chrome-plugin - bi_aatf: bi_aatf using ATF-chrome-plugin - bi_plt: bi_plt using ATF-chrome-plugin - dom: dom using ATF-chrome-plugin - ii_aatf: ii_aatf using ATF-chrome-plugin - ii_plt: ii_plt using ATF-chrome-plugin - last_css: last_css using ATF-chrome-plugin - last_img: last_img using ATF-chrome-plugin - last_js: last_js using ATF-chrome-plugin - nb_ress_css: nb_ress_css using ATF-chrome-plugin - nb_ress_img: nb_ress_img using ATF-chrome-plugin - nb_ress_js: nb_ress_js using ATF-chrome-plugin - num_origins: num_origins using ATF-chrome-plugin - num_ressources: num_ressources using ATF-chrome-plugin - oi_aatf: oi_aatf using ATF-chrome-plugin - oi_plt: oi_plt using ATF-chrome-plugin - plt: plt using ATF-chrome-pluginRegarding progressionCurves.csv, the columns are: - id: Unique identification of an experiment (consisting of an index 'set of conditions' and an index 'current page') - url: Url of the current page. SUBPAGE stands for a path. - run: Current run (linked with index of the config for WPT) - filename: Filename of the pcap - fullname: Fullname of the pcap - har_size: Size of the HAR for this experiment, - pagedata_size: Size of the page data report - pcap_size: Size of the pcap - App Byte Index (ms): Application Byte Index as computed from the har file (in the browser) - bytesIn_APP: Total bytes in as seen in the browser, - bytesIn_NET: Total bytes in as seen in the network, - X_BI_net: Network Byte Index computed from the pcap file (in the network) - X_bin_0_for_B_completion to X_bin_99_for_B_completion: X_bin_k_for_B_completion is the bytes progress reached after k*100 millisecondsIf you use these datasets in your research, you can reference to the appropriate paper:@inproceedings{qoeNetworking2020, title={Revealing QoE of Web Users from Encrypted Network Traffic}, author={Huet, Alexis and Saverimoutou, Antoine and Ben Houidi, Zied and Shi, Hao and Cai, Shengming and Xu, Jinchun and Mathieu, Bertrand and Rossi, Dario}, booktitle={2020 IFIP Networking Conference (IFIP Networking)}, year={2020}, organization={IEEE}}

The recording was created with the General Purpose Network Testing Framework:

https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework

At The Good, we love to use the phrase, “Let’s test it.” There’s no sense in making assumptions or guessing what an audience wants. It’s smarter – and better for your budget – to run experiments in order to determine what actually boosts your conversion rate. A/B testing is the standard experimentation methodology for digital marketing. […]