CNS Core: Small: Core Scheduling Techniques and Programming Abstractions for Scalable Serverless Edge Computing Engine

CNS Core：小型：可扩展无服务器边缘计算引擎的核心调度技术和编程抽象

• 批准号: 2322919
• 负责人: Liting Hu
• 金额: $ 60万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322919&HistoricalAwards=false
• 关键词: CNS; Core; Small; Scheduling; Techniques

The proliferation of 5G and beyond facilitates the advancement of next-generation technologies, including smart cities, self-driving cars, online video gaming, virtual reality, and augmented reality. This necessitates a re-evaluation of how these services are characterized and deployed. Serverless computing is an emerging paradigm, referring to a software architecture where an application is decomposed into triggers (also called events) and actions (also called functions), and there is a platform that provides seamless hosting and execution environment, making it easy to develop, manage, scale, and operate them. This project aims to build a next-generation serverless edge computing engine that empowers a vast number of distributed edge applications, such as data analytics, edge AI, and media streaming, to run efficiently at the edge through the Function-as-a-Service model. This project breaks the traditional abstractions and redefines new abstractions in the scheduling layer and storage layer that collectively deliver a scalable serverless edge computing engine. First, a full decentralized scheduling architecture is proposed, which dramatically improves the scalability of the proposed serverless edge computing engine. Second, an active object store abstraction is proposed, which is used for storing and sharing application states in a user-customizable manner. Third, the proposed serverless edge computing engine is implemented on top of the open-source software stacks. The evaluation is multi-pronged and includes micro-benchmarks for component testing and real-world applications for overall system testing. The results of the research are integrated into the undergraduate and graduate systems courses. The source code, datasets, tools, techniques, and new course materials developed in this research will be made publicly available.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.

5G及以后的扩散有助于下一代技术的发展，包括智能城市，自动驾驶汽车，在线视频游戏，虚拟现实和增强现实。这需要重新评估这些服务的表征和部署。无服务器计算是一种新兴的范式，指的是将应用程序分解为触发器（也称为事件）和操作（也称为功能）的软件体系结构，并且有一个平台可提供无缝的托管和执行环境，从而易于开发，管理，管理，扩展和操作它们。 该项目旨在构建下一代无服务器边缘计算引擎，该引擎能够赋予大量分布式边缘应用程序，例如数据分析，边缘AI和媒体流，以通过功能-AS-A-Service模型在边缘有效地运行。该项目打破了传统的抽象，并在调度层和存储层中重新定义了新的抽象，这些抽象共同提供了可扩展的无服务器边缘计算引擎。首先，提出了完整的分散调度架构，该体系结构极大地提高了所提出的无服务器边缘计算引擎的可扩展性。其次，提出了一个活动的对象存储抽象，该抽象用于以用户可定制的方式存储和共享应用程序状态。第三，提出的无服务器边缘计算引擎是在开源软件堆栈顶部实现的。评估是多管齐下的，包括用于组件测试的微基准和用于整体系统测试的现实世界应用。研究结果融入了本科和研究生系统课程中。本研究中开发的源代码，数据集，工具，技术和新课程材料将被公开可用。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来提供支持。