The rapid growth of data generated from Internet of Things (IoTs) such as smart phones and smart home devices presents new challenges to cloud computing in transferring, storing, and processing the data. With increasingly more powerful edge devices, edge computing, on the other hand, has the potential to better responsiveness, privacy, and cost efficiency. However, resources across the cloud and edge are highly distributed and highly diverse. To address these challenges, this paper proposes EdgeFaaS, a Function-as-a-Service (FaaS) based computing framework that supports the flexible, convenient, and optimized use of distributed and heterogeneous resources across IoT, edge, and cloud systems. EdgeFaaS allows cluster resources and individual devices to be managed under the same framework and provide computational and storage resources for functions. It provides virtual function and virtual storage interfaces for consistent function management and storage management across heterogeneous compute and storage resources. It automatically optimizes the scheduling of functions and placement of data according to their performance and privacy requirements. EdgeFaaS is evaluated based on two edge workflows: video analytics workflow and federated learning workflow, both of which are representative edge applications and involve large amounts of input data generated from edge devices.

翻译：随着智能手机和智能家居等物联网设备生成数据的快速增长，云计算在数据传输、存储和处理方面面临新的挑战。另一方面，随着边缘设备日益强大，边缘计算在响应速度、隐私保护和成本效益方面具有更大潜力。然而，云与边缘之间的资源高度分布式且高度异构。为应对这些挑战，本文提出EdgeFaaS——一种基于函数即服务的计算框架，支持灵活、便捷且优化地使用物联网、边缘和云系统中的分布式异构资源。EdgeFaaS允许集群资源与独立设备在同一框架下进行管理，并为函数提供计算与存储资源。它通过虚拟函数接口和虚拟存储接口，在异构计算与存储资源间实现一致的函数管理与存储管理。该系统能根据性能需求与隐私要求，自动优化函数调度与数据部署策略。基于两个典型边缘工作流（视频分析工作流与联邦学习工作流）对EdgeFaaS进行评估，这两种工作流均涉及边缘设备产生的大量输入数据，是具代表性的边缘应用场景。