LivingBench: An IoT/Edge Platform Benchmark Based on an Environmental Observation Use-Case

In recent years, a number of edge computing platforms have been proposed to process data produced by IoT sensors. Performing computation close to the sources of data allows faster insight and greater reliability at a lower cost compared to traditional cloud-based deployments. However, designers of IoT/edge platforms face difficult issues. In particular, exercising and testing a new platform in conditions that approach a real deployment requires a sufficient number of standard benchmarking systems capable of generating realistic workloads. In this paper, we propose LivingBench, a benchmarking tool with the capability of incorporating real or synthetic workload injection, developed to exercise edge computing systems. LivingBench integrates a real-world data trace captured in an environmental observatory, together with a collection of actual applications designed for processing these data, and a load injector tool capable of replaying a (possibly pre-processed) trace to benchmark an MQTT-based edge system. We describe the architecture of LivingBench and show how it may be used to evaluate the maximum data processing capacity of an edge system under test.