Author(s): Anna Schmidt, James Morgan and Maria Gonzalez

Abstract: Fog computing and cloud computing are two paradigms in distributed computing, each with unique advantages and challenges. While cloud computing is established and widely used in industries for large-scale data processing and storage, fog computing is an emerging model that enhances the capabilities of cloud systems by distributing computing resources closer to the edge of the network. This research provides a comparative analysis of fog and cloud computing in terms of efficiency and performance. Cloud computing typically offers centralized storage and computational resources, whereas fog computing brings computation closer to end users and devices, reducing latency and bandwidth usage. This paper evaluates both models by considering factors such as latency, energy consumption, scalability, and security. By analyzing several case studies and performance metrics, this research highlights the strengths and weaknesses of each paradigm. It also investigates how fog computing can address some of the limitations of cloud computing, particularly in applications requiring real-time processing, such as Internet of Things (IoT) devices. The research aims to offer insights into the potential for integrating both computing models to optimize performance in diverse environments. The results indicate that while cloud computing excels in large-scale data management and processing, fog computing outperforms cloud systems in real-time applications due to its proximity to end devices. This paper concludes by discussing future research directions to further improve both fog and cloud computing efficiency, particularly in hybrid models.

DOI: 10.33545/26633582.2026.v8.i1a.241

Pages: 09-13 | Views: 74 | Downloads: 33

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