SHF: Small: Towards High Performance Serverless Edge Computing for Data-intensive Applications

SHF：小型：面向数据密集型应用程序的高性能无服务器边缘计算

• 批准号: 2230620
• 负责人: Yuanyuan Yang
• 金额: $ 60万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230620&HistoricalAwards=false
• 关键词: SHF; Small; Towards; Performance; Serverless

Edge computing has become an indispensable booster for the rapid development of Internet-of-Things. It provides users not only low-delay and high-bandwidth services but also unprecedented security and privacy guarantee. However, the unique challenges of network edge, such as resource limitation and device heterogeneity, greatly increase the difficulty of developing and managing edge applications. Researchers have turned to emerging serverless computing paradigm for efficient and cost-effective solutions. The light-weight virtualization of serverless edge computing largely mitigates the resource shortage of edge environments while providing extra flexibility. Furthermore, edge application developers are completely liberated from complicated management of edge resources, facilitating the portability and short time-to-market of edge applications. Nevertheless, serverless edge computing is still impeded by the inherent natures of network edge. The computation/data geo-dispersion and device heterogeneity in edge environments may introduce long transmission and computation delay to the initialization and execution of data-intensive edge functions. The resource limitation and volatile network conditions further exacerbate the negative impact and increase the risk of network congestion. This project aims to systematically investigate the challenging issues towards building a high performance serverless edge computing framework for data-intensive applications. This project provides a hardware/software co-design to construct a novel serverless edge computing framework for data-intensive applications. More specifically, it consists of following inter-related research tasks: (1) develop a hardware-enhanced architecture for accelerating data processing, transmission and distribution of serverless edge computing; (2) design a data-aware computation orchestration system that adapts to unique edge conditions and the data acceleration architecture; (3) develop a computation-aware data orchestration system that adjusts the data acceleration architecture dynamically; (4) conduct a comprehensive performance evaluation through extensive simulations and implementation of a prototype of the framework. The outcome of this project will not only enhance the performance of many current data-intensive edge functions, but also facilitate the edge adoption for numerous future applications that rely on efficient data processing and transmission. As edge computing is penetrating into all aspects of human lives, this research will have a profound impact on the society. The project trains graduate students and promotes the participation of female students in computer engineering. The important findings of this project will be integrated into a graduate course and disseminated to research community and industry by way of conferences, journals and website access.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.

边缘计算已成为快速发展的必不可少的助推器。它不仅为用户提供低延迟和高宽带宽度服务，还为前所未有的安全性和隐私保证提供了服务。但是，网络边缘的独特挑战（例如资源限制和设备异质性）极大地增加了开发和管理边缘应用程序的困难。研究人员已转向新兴的无服务器计算范式，以获得有效且具有成本效益的解决方案。无服务器边缘计算的轻巧虚拟化在很大程度上减轻了边缘环境的资源短缺，同时提供了额外的灵活性。此外，Edge应用程序开发人员完全摆脱了对边缘资源的复杂管理，促进了Edge应用程序的可移植性和短暂的上市时间。然而，无服务器边缘计算仍然受到网络边缘固有的本质的阻碍。边缘环境中的计算/数据地理分散和设备异质性可能会引入长时间的传输和计算延迟，以引入数据密集型边缘功能的初始化和执行。资源限制和挥发性网络条件进一步加剧了负面影响并增加网络拥塞的风险。该项目旨在系统地研究为数据密集型应用程序构建高性能无服务器边缘计算框架的具有挑战性的问题。该项目提供了一个硬件/软件共同设计，可为数据密集型应用程序构建新颖的无服务器边缘计算框架。更具体地说，它包括遵循相互关联的研究任务：（1）开发与硬件增强的体系结构，用于加速无服务器边缘计算的数据处理，传输和分布； （2）设计一个数据感知计算编排系统，可适应唯一的边缘条件和数据加速体系结构； （3）开发一个计算意识到的数据编排系统，该系统动态调整数据加速架构； （4）通过广泛的模拟和实施该框架的原型进行全面的绩效评估。该项目的结果不仅将提高许多当前数据密集型边缘功能的性能，而且还可以促进依赖有效数据处理和传输的众多未来应用的边缘采用。随着边缘计算正在渗透到人类生活的各个方面，这项研究将对社会产生深远的影响。该项目培训研究生，并促进女学生参与计算机工程。该项目的重要发现将纳入研究生课程中，并通过会议，期刊和网站访问将其传播到研究社区和行业。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准通过评估来获得支持的。