The Internet of Things is transforming our society by monitoring users and infrastructures' behavior to enable new services that will improve life quality and resource management. These applications require a vast amount of localized information to be processed in real-time so, the deployment of new fog computing infrastructures that bring computing closer to the data sources is a major concern. In this context, we present Mercury, a Modeling, Simulation, and Optimization (M&S&O) framework to analyze the dimensioning and the dynamic operation of real-time fog computing scenarios. Our research proposes a location-aware solution that supports data stream analytics applications including FaaS-based computation offloading. Mercury implements a detailed structural and behavioral simulation model, providing fine-grained simulation outputs, 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 assistance scenario, offering a detailed comparison with other state-of-the-art simulators.
@article{arxiv.2312.02172,
title = {Mercury: A modeling, simulation, and optimization framework for data stream-oriented IoT applications},
author = {Román Cárdenas and Patricia Arroba and Roberto Blanco and Pedro Malagón and José L. Risco-Martín and José M. Moya},
journal= {arXiv preprint arXiv:2312.02172},
year = {2023}
}