NeTS: Small: Data Driven Mobile Web Performance

世界越来越依赖于网络服务；它们现在是日常生活中商业、个人和社会方面的重要组成部分。不幸的是，用户继续受到无数性能问题的困扰，例如，视频在缓冲时停滞，网页加载时间缓慢，网页崩溃。出现这些性能问题有多种原因，主要原因是缺乏正确配置和充分调优的网络协议和算法，无法提供所需的服务质量。此外，随着web服务和技术的不断增长和扩展，这些配置不良的网络的性能影响可能会变得更加广泛和明显。该项目的愿景是开发一种管理和配置web服务器的替代方法，以满足最终用户的性能期望：一种通过定制web服务器的配置来提高性能的方法，以匹配每个应用程序的具体情况和最终用户的技术。如果成功，该建议将从根本上影响被广泛接受的服务管理原则，并将使广泛的用户受益于不断增长的基本web服务集的有效操作。该项目提出了一个雄心勃勃的研究议程，该议程基于数据驱动的web服务堆栈专门化，其中堆栈学习每个用户的最佳协议优化和参数，反过来，使用这些信息来专门化或调整web堆栈以改善最终用户的体验。提议的研究有五个重点：首先，该项目将对协议配置选项之间的相互作用进行大规模系统研究，例如优化、设备配置文件、网络复杂性和网络条件，并开发工具来分析和解释这些相互作用的含义。其次，它将开发一个管理web服务器的新架构，为收集遥测数据和配置协议参数和优化提供灵活和统一的接口。第三，研究人员将开发一个动态学习框架，该框架可扩展且准确地学习和预测每个用户的最佳配置。第四，该项目建议开发一种新颖的编程接口，通过提供仅在客户端可用的反馈和元数据，使最终用户及其设备能够参与学习过程。最后，研究人员将考虑实现这种新方法所需的一些设计和实现挑战，包括适应网络条件的急剧变化，处理配置参数和用户体验之间的复杂关系，进行实时预测等。该项目将积极开展任务，通过在各级教育中开展教育推广，实现更广泛的影响，重点是本科生和高中生，以及在科学、技术、工程和数学领域代表性不足的少数民族。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The world is growing increasingly dependent on web services; they are now a vital part of business, personal and social aspects of daily life. Unfortunately, users continue to be plagued by a myriad of performance problems, e.g., videos that stall while buffering, slow web page load times, and web pages that crash. These performance problems occur for multiple reasons, the principal one being a lack of correctly configured and adequately tuned networking protocols and algorithms that can deliver the desired quality of service. Moreover, as the set of web services and technologies continues to grow and broaden, the performance implications of these poorly configured networks are likely to become more widespread and apparent. The project vision is to develop an alternative approach to managing and configuring web servers that can meet end user's performance expectations: a method which improves performance by tailoring the web server's configurations to match the specifics of each application and end user's technology. If successful, this proposal will fundamentally impact widely accepted principles for managing services and will enable a broad range of users to benefit from efficient operation of the growing set of essential web services.This project proposes an ambitious research agenda based on a data-driven specialization of the web serving stacks, wherein the stack learns the optimal protocol optimizations and parameters for each user and, in turn, uses this information to specialize or tune the web stack to improve the end user's experience. The proposed research has five thrusts: First, the project will conduct a large-scale systematic study on the interactions between protocol configuration options, e.g., optimizations, device profiles, web complexity, and network conditions and develop tools to analyze and interpret the implications of these interactions. Second, it will develop a new architecture for managing web servers that provides a flexible and uniform interface for collecting telemetry data and configuring protocol parameters and optimizations. Third, the researchers will develop a dynamic learning framework that scalably and accurately learns and predicts the optimal configuration for each user. Fourth, the project proposes to develop a novel programming interface that enables end-users and their devices to participate in the learning process by providing feedback and meta-data that is only available on the client side. Finally, the researchers will consider a number of design and implementation challenges necessary to realize this new approach, including adjusting to drastic changes in network conditions, tackling complex relationships between configuration parameters and user experience, making real-time predictions, etc. This project will actively undertake tasks to realize broader impact through educational outreach across all levels of education, with a focus on undergraduate and high school students, and under-represented minorities in Science, Technology, Engineering, and Mathematics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.