  The emergence of the Internet of Things has seen the introduction of numerous
connected devices used for the monitoring and control of even Critical
Infrastructures. Distributed stream processing has become key to analyzing data
generated by these connected devices and improving our ability to make
decisions. However, optimizing these systems towards specific Quality of
Service targets is a difficult and time-consuming task, due to the large-scale
distributed systems involved, the existence of so many configuration
parameters, and the inability to easily determine the impact of tuning these
parameters.
  In this paper we present an approach for the effective testing of system
configurations for critical IoT analytics pipelines. We demonstrate our
approach with a prototype that we called Timon which is integrated with
Kubernetes. This tool allows pipelines to be easily replicated in parallel and
evaluated to determine the optimal configuration for specific applications. We
demonstrate the usefulness of our approach by investigating different
configurations of an exemplary geographically-based traffic monitoring
application implemented in Apache Flink.
