Author(s): Olga Ivanova

Abstract: Cloud computing has become an essential technology in modern computing, providing on-demand access to resources and services over the internet. However, many applications, especially those involving the Internet of Things (IoT), require ultra-low latency and real-time processing capabilities, which traditional cloud computing struggles to provide. To address this issue, edge and fog computing paradigms have been introduced. Edge computing processes data closer to the end-user devices, while fog computing extends cloud capabilities to the edge by distributing computational tasks across a network of edge devices. These paradigms aim to reduce latency, improve bandwidth efficiency, and enhance overall system performance. This paper explores the integration of edge and fog computing in cloud environments, focusing on their potential to support low-latency applications. By analyzing the architectural designs, advantages, and challenges associated with these computing models, this paper provides a comprehensive overview of their role in enabling real-time processing for IoT and other latency-sensitive applications. Furthermore, it presents case studies demonstrating the successful implementation of these technologies in various industries such as healthcare, smart cities, and autonomous vehicles. Despite the benefits, issues such as security, scalability, and resource management remain significant challenges. The paper concludes by discussing future directions for edge and fog computing, including potential solutions to these challenges and their implications for the next generation of low-latency applications.

DOI: 10.33545/27075907.2026.v7.i1a.118

Pages: 17-20 | Views: 34 | Downloads: 14

Download Full Article: Click Here