A Trifecta for Low-Latency Real-Time Analytics: Optimizing Cloud-Based Applications with Edge-Fog-Cloud Integration Architecture

Abstract

The research paper presents a comprehensive study on addressing the latency challenges faced by real-time applications through innovative architectural solutions. With the exponential growth of data generation, the demand for efficient data processing architectures has become increasingly urgent. The paper introduces an integrated approach that combines edge, fog, and cloud computing to overcome the limitations of traditional cloud-based systems. By leveraging edge computing for local data processing, fog computing for intermediary aggregation, and cloud computing for advanced analytics, the architecture optimizes resource utilization and reduces latency, ensuring faster decision-making and improved responsiveness for critical tasks. Key components include adaptive resource management algorithms, AI/ML integration at the edge, and robust security protocols. The paper highlights the significance of these contributions in advancing distributed computing and outlines future research directions to further enhance Edge-Fog-Cloud integration for various application domains.