  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.
