Fog Computing Simulators: A Comprehensive Research and Analytical Study

Abstract

Fog computing, a novel paradigm for distributed computing, has found extensive applications in critical sectors like healthcare. This study is dedicated to setting up and evaluating network properties crucial for real-time decision-making systems. Specifically, it comprehensively and analytically assesses two leading fog computing simulators, YAFS and LEAF. By focusing on key performance metrics—memory usage, CPU consumption, and execution latency—the research aims to clearly delineate the capabilities and limitations of each simulator. Through meticulous comparative analysis, the study identifies which simulator offers superior efficiency and scalability in modelling complex fog computing environments within healthcare. Moreover, the paper aims to highlight both the strengths and weaknesses of YAFS and LEAF, providing foundational insights to inform the deployment of fog computing solutions in healthcare settings. This research not only examines the technical properties and performance of these simulators but also explores broader implications of adopting fog computing over traditional cloud architectures. Ultimately, the findings aim to serve as a valuable guide for researchers and practitioners in selecting the most suitable simulation tools, thereby facilitating the enhanced design and optimization of fog-based applications.