In this paper, we address the problem of supporting stateful workflows following a Function-as-a-Service (FaaS) model in edge networks. In particular we focus on the problem of data transfer, which can be a performance bottleneck due to the limited speed of communication links in some edge scenarios and we propose three different schemes: a pure FaaS implementation, StateProp, i.e., propagation of the application state throughout the entire chain of functions, and StateLocal, i.e., a solution where the state is kept local to the workers that run functions and retrieved only as needed. We then extend the proposed schemes to the more general case of applications modeled as Directed Acyclic Graphs (DAGs), which cover a broad range of practical applications, e.g., in the Internet of Things (IoT) area. Our contribution is validated via a prototype implementation. Experiments in emulated conditions show that applying the data locality principle reduces significantly the volume of network traffic required and improves the end-to-end delay performance, especially with local caching on edge nodes and low link speeds.

翻译：在本文中,我们处理支持边缘网络中基于功能即服务模式(FaaS)的状态性工作流程的问题,我们尤其注重数据传输问题,因为在某些边缘情景中通信联系速度有限,这可能成为绩效瓶颈,我们提出三种不同的计划:纯粹的FaaS实施,PateProp,即在整个职能链中传播应用状态,以及国家本地,即国家将本地保持运行功能和仅按需要检索的工人的功能的解决方案。然后,我们将拟议的计划扩大到以定向环绕图(DAGs)为模型的更一般的应用,这些应用包括广泛的实际应用,例如,在Tings(IoT)互联网领域。我们的贡献通过原型实施得到验证。在模拟条件下进行的实验表明,应用数据地点原则会大大减少了所需的网络流量,并改进了端至端的延迟性能,特别是以边缘节点和低连接速度为模型的本地缓存。