CNS Core: Medium: A User-centric Adaptation Framework for Edge-Native Applications

该提案解决了一类称为“边缘原生应用程序”的新应用程序，这些应用程序在诸如帮助残疾用户，加强视频流中的隐私以及提高即时制造的生产力等领域具有巨大的社会价值。边缘原生应用程序同时是计算密集型的，占用带宽，并且对延迟敏感。这些属性对可伸缩性提出了根本性的挑战。这项建议的目标是开发新的技术，有效地支持大量的用户，这样的应用程序，而不会损害他们的体验质量（QoE）。Thrust-1研究了传感器数据的设备处理和自适应采样如何减少边缘基础设施的负载，同时最大限度地降低对QoE的影响。 这一推动还在操作系统和应用程序之间创建了一个API以进行适配。Thrust-2探索如何高效、无缝地将工作从过度使用的边缘基础设施转移到未充分利用的站点。它研究了基于虚拟机（VM）封装的应用程序透明的方法，以及旨在节省数据传输的应用程序优化的方法。Thrust-3创建了研究QoE的工具和机制。它对动态收集的基于历史的数据使用机器学习，构建特定于用户和特定于应用程序的权衡模型，以将应用程序保真度映射到QoE。它还为边缘原生应用程序的QoE调试创建了工具。Thrust-4探讨了如何在不执行用户研究的情况下评估动态变化QoE的多保真度应用程序。它开发了一种新的评估方法，该方法基于合成用户的概念，也被称为“机器人”。通过与行业和当地政府的密切合作，这项研究将加速变革性边缘原生应用的出现。通过与教育和课程开发的整合，这项研究将为本科和研究生阶段的计算机科学，电气和计算机工程以及人机交互学生提供独特的学习机会。由于应用程序在这项研究中的核心作用，它为不同群体的个人提供了许多研究机会，包括那些来自代表性不足的群体。在这项研究过程中开发的软件将通过GitHub（http：//www.example.com）开源发布。github.com基准和实验数据将在机构网站（http：//www.example.com）上公布。elijah.cs.cmu.edu所有研究成果将在项目结束后或数据公布后（以先到者为准）至少五年内公开并积极维护。该奖项反映了NSF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。

This proposal addresses a new class of applications called "edge-native applications" that have enormous societal value in domains such as assisting handicapped users, enforcing privacy in video streams, and enhancing the productivity of just-in-time manufacturing. Edge-native applications are simultaneously compute-intensive, bandwidth-hungry, and latency-sensitive. These attributes pose a fundamental challenge to scalability. The goal of this proposal is to develop new techniques for efficiently supporting large numbers of users of such applications, without hurting their quality of experience (QoE).The proposed research is organized into four thrusts. Thrust-1 investigates how on-device processing and adaptive sampling of sensor data can reduce load on edge infrastructure, while minimally impacting QoE. This thrust also creates an API between the operating system and applications for adaptation. Thrust-2 explores how to efficiently and seamlessly move work from overcommitted edge infrastructure to underutilized sites. It investigates both an application-transparent approach that is based on virtual-machine (VM) encapsulation, and an application-optimized approach that seeks to be frugal in data transmission. Thrust-3 creates tools and mechanisms to study QoE. Using machine learning on history-based data that is dynamically collected, it builds models of user-specific and application-specific tradeoffs for mapping application fidelity to QoE. It also creates tools for QoE debugging of edge-native applications. Thrust-4 explores how multi-fidelity applications that dynamically vary QoE can be evaluated without performing user studies. It develops a new evaluation methodology that is based on the concept of synthetic users, also known as "droids".Through close partnership with industry and local government, this research will accelerate the emergence of transformative edge-native applications. Through integration with education and curriculum development, this research will provide unique learning opportunities for students in Computer Science, Electrical and Computer Engineering, and Human-Computer Interaction at the undergraduate and graduate levels. Because of the central role of applications in this research, it offers many research opportunities for a diverse group of individuals, including those from under-represented groups.Software developed in the course of this research will be released open source via GitHub (http://github.com). Benchmarks and experimental data will be released on an institutional website (http://elijah.cs.cmu.edu). All results generated through this research will be available and actively maintained for at least five years after the conclusion of the project or after the publication of the data, whichever is first.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.