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

CNS Core：Medium：面向边缘原生应用程序的以用户为中心的适配框架

基本信息

批准号：
2106862
负责人：
Mahadev Satyanarayanan
金额：
$ 120万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106862&HistoricalAwards=false
关键词：
CNS Core Medium User centric

项目摘要

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.

该提案针对的是一类新的应用程序，称为“边缘本地应用程序”，这些应用程序在帮助残疾用户、加强视频流中的隐私保护和提高即时制造的生产率等领域具有巨大的社会价值。边缘本地应用同时具有计算密集型、带宽密集型和延迟敏感型特点。这些属性对可伸缩性构成了根本挑战。该方案的目标是开发新的技术，在不损害用户体验质量的情况下，有效地支持大量此类应用程序的用户。推力-1调查了传感器数据的设备上处理和自适应采样如何在最大限度地降低边缘基础设施的负载的同时，将对QOE的影响降至最低。这一推力还在操作系统和应用程序之间创建了一个API以进行适配。推力-2探讨了如何高效、无缝地将工作从过度使用的边缘基础设施转移到未得到充分利用的站点。它既研究了基于虚拟机(VM)封装的应用程序透明方法，也研究了寻求节约数据传输的应用程序优化方法。推力-3创造了研究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，建立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。它还创建了用于边缘本地应用程序的QOE调试的工具。推力-4探索了如何在不执行用户研究的情况下评估动态变化的QOE的多保真应用程序。它开发了一种新的评估方法，基于合成用户的概念，也被称为“机器人”。通过与行业和地方政府的密切合作，这项研究将加速出现变革性的边缘原生应用。通过与教育和课程开发相结合，这项研究将为本科生和研究生提供计算机科学、电气和计算机工程以及人机交互方面的独特学习机会。由于应用程序在这项研究中的核心作用，它为不同的个人群体提供了许多研究机会，包括那些来自代表不足的群体的人。在此研究过程中开发的软件将通过GitHub(http://github.com).)发布基准和实验数据将在机构网站(http://elijah.cs.cmu.edu).)上发布通过这项研究产生的所有结果将在项目结束后或数据公布后至少五年内(以较早者为准)可用并积极维护。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Semantic Fast-Forwarding for Video Training Set Construction

视频训练集构建的语义快进

DOI：
10.1145/3572864.3580331
发表时间：
2023
期刊：
HotMobile '23: Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications
影响因子：
0
作者：
Feng, Ziqiang;Satyanarayanan, Mahadev
通讯作者：
Satyanarayanan, Mahadev

Accelerating Silent Witness Storage

加速沉默证人存储

DOI：
10.1109/mm.2022.3193048
发表时间：
2022
期刊：
IEEE Micro
影响因子：
3.6
作者：
Satyanarayanan, Mahadev;Feng, Ziqiang;George, Shilpa;Harkes, Jan;Iyengar, Roger;Turki, Haithem;Pillai, Padmanabhan
通讯作者：
Pillai, Padmanabhan

Balancing privacy and serendipity in cyberspace

平衡网络空间中的隐私和偶然性

DOI：
10.1145/3508396.3512873
发表时间：
2022
期刊：
Proceedings of the 23rd Annual International Workshop on Mobile Computing Systems and Applications
影响因子：
0
作者：
Satyanarayanan, Mahadev;Davies, Nigel;Taft, Nina
通讯作者：
Taft, Nina

Experience with Using Synthetic Training Images for Wearable Cognitive Assistance