发表机构
Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对边缘计算资源异构且分布带来的挑战，提出EdgeFaaS框架，通过函数和存储虚拟化抽象资源并提供虚拟接口。在真实测试平台上对视频分析等三个工作流实现与评估，展示其可用性，让用户能灵活探索工作流在分布式异构资源上的部署配置及权衡。
AI 中文摘要
边缘计算带来了独特挑战，因为边缘资源在能力和容量上高度多样且分布广泛。现有分布式计算框架无法充分处理这种异构性和分布性。本文提出EdgeFaaS，一种新颖的基于函数的边缘计算框架，能让边缘应用有效利用物联网、边缘和云的异构资源进行计算。它提出函数虚拟化和存储虚拟化来抽象物理资源，并提供一致虚拟接口。还通过在100多个地理分布的物联网设备、边缘服务器和云服务的真实测试平台上对视频分析、联邦学习和音频分类的三个代表性工作流进行实现和评估来展示其可用性。EdgeFaaS允许用户灵活探索这些工作流在分布式异构资源上的部署配置，例如改变视频处理管道的函数放置来研究计算与通信成本的权衡，以及在分层联邦学习系统中灵活调整集群数量和大小来探索训练精度与速度的权衡。
英文摘要
Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing frameworks cannot adequately handle this level of heterogeneity and distribution. This paper proposes EdgeFaaS, a novel function-based edge computing framework to enable edge applications to effectively utilize heterogeneous resources distributed across the Internet of Things (IoT), edge, and cloud for computing. It proposes function virtualization and storage virtualization to abstract distributed and heterogeneous physical resources and provides consistent virtual interfaces for deploying and executing functions and storing and accessing data. EdgeFaaS provides comprehensive support to diverse edge computing workflows, and at the same time allows users to flexibly adjust the configurations and explore various important tradeoffs. To demonstrate its usability, the paper also presents the implementation and evaluation of three representative workflows on EdgeFaaS for video analytics, federated learning, and audio classification, on a real testbed of 100+ geographically distributed IoT devices, edge servers, and cloud services. EdgeFaaS allows users to flexibly explore the deployment configurations of these workflows over distributed and heterogeneous resources. For example, users can easily vary the function placement of the video processing pipeline across IoT, edge, and cloud resources and study the tradeoff between computation and communication costs; users can also flexibly adjust the cluster count and size in the hierarchical federated learning system and explore the tradeoff between training accuracy and speed.
Comments2026 IEEE International Conference on Edge Computing (EDGE)
