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

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.