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The Internet of Things is transforming our society by monitoring users andinfrastructures' behavior to enable new services that will improve life qualityand resource management. These applications require a vast amount of localizedinformation to be processed in real-time so, the deployment of new fogcomputing infrastructures that bring computing closer to the data sources is amajor concern. In this context, we present Mercury, a Modeling, Simulation, andOptimization (M&S&O) framework to analyze the dimensioning and the dynamicoperation of real-time fog computing scenarios. Our research proposes alocation-aware solution that supports data stream analytics applicationsincluding FaaS-based computation offloading. Mercury implements a detailedstructural and behavioral simulation model, providing fine-grained simulationoutputs, and is described using the Discrete Event System Specification (DEVS)mathematical formalism, helping to validate the model's implementation.Finally, we present a case study using real traces from a driver assistancescenario, offering a detailed comparison with other state-of-the-artsimulators.

Mercury: A modeling, simulation, and optimization framework for data stream-oriented IoT applications