In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.

翻译：在云层处理中,在边缘生成的数据由云层资源实时处理,已提议采用分布式流处理(DSP)和函数即服务(FaaS)来实施此类事件处理应用程序。FaS强调快速开发和简易操作,而DSP则强调高效处理大数量的数据。尽管在结构上存在差异,但两者都可用于模拟和实施松散的组合工作图。在本文中,我们从成本角度考虑选择FaaS和DSP。我们使用云层FaS和DSP执行Theodolite基准套件的无国籍和状态性工作流程。在一次广泛的评估中,我们展示应用类型、云服务供应商和运行环境如何影响应用部署的成本,并为云层工程师制定决策准则。