The massive growth of mobile and IoT devices demands geographically distributed computing systems for optimal performance, privacy, and scalability. However, existing edge-to-cloud serverless platforms lack location awareness, resulting in inefficient network usage and increased latency. In this paper, we propose GeoFaaS, a novel edge-to-cloud Function-as-a-Service (FaaS) platform that leverages real-time client location information for transparent request execution on the nearest available FaaS node. If needed, GeoFaaS transparently offloads requests to the cloud when edge resources are overloaded, thus, ensuring consistent execution without user intervention. GeoFaaS has a modular and decentralized architecture: building on the single-node FaaS system tinyFaaS, GeoFaaS works as a stand-alone edge-to-cloud FaaS platform but can also integrate and act as a routing layer for existing FaaS services, e.g., in the cloud. To evaluate our approach, we implemented an open-source proof-of-concept prototype and studied performance and fault-tolerance behavior in experiments.

翻译：移动设备与物联网设备的爆炸式增长，对地理分布式计算系统提出了需求，以实现最优性能、隐私保护与可扩展性。然而，现有的边云融合无服务器平台缺乏位置感知能力，导致网络使用效率低下且延迟增加。本文提出GeoFaaS，一种新颖的边云融合函数即服务平台，该平台利用实时客户端位置信息，将请求透明地调度至最近可用的FaaS节点执行。当边缘资源过载时，GeoFaaS可透明地将请求卸载至云端，从而在无需用户干预的情况下保障稳定执行。GeoFaaS采用模块化去中心化架构：基于单节点FaaS系统tinyFaaS构建，既可作为独立的边云融合FaaS平台运行，也能作为路由层与现有FaaS服务（例如云端服务）集成协同。为验证方案有效性，我们实现了开源概念验证原型，并通过实验评估了其性能与容错行为。