Fog data processing systems provide key abstractions to manage data and event processing in the geo-distributed and heterogeneous fog environment. The lack of standardized benchmarks for such systems, however, hinders their development and deployment, as different approaches cannot be compared quantitatively. Existing cloud data benchmarks are inadequate for fog computing, as their focus on workload specification ignores the tight integration of application and infrastructure inherent in fog computing. In this paper, we outline an approach to a fog-native data processing benchmark that combines workload specifications with infrastructure specifications. This holistic approach allows researchers and engineers to quantify how a software approach performs for a given workload on given infrastructure. Further, by basing our benchmark in a realistic IoT sensor network scenario, we can combine paradigms such as low-latency event processing, machine learning inference, and offline data analytics, and analyze the performance impact of their interplay in a fog data processing system.

翻译：雾数据处理系统提供了关键抽象机制，用于管理地理分布式异构雾环境中的数据与事件处理。然而，此类系统缺乏标准化基准测试，导致不同方法无法进行定量比较，进而阻碍了其开发与部署。现有的云数据基准测试不适用于雾计算，因其专注于工作负载规范而忽略了雾计算中固有的应用与基础设施紧密耦合特性。本文提出了一种雾原生数据处理基准测试方法，将工作负载规范与基础设施规范相结合。这种整体性方法使研究人员和工程师能够量化软件方案在特定基础设施上处理给定工作负载时的性能表现。此外，通过将基准测试建立在真实物联网传感器网络场景中，我们能够融合低延迟事件处理、机器学习推理与离线数据分析等多种范式，并分析其交互作用对雾数据处理系统性能的影响。