Parallel Processing in Distributed and Hybrid Cloud-Fog Architectures: A Systematic Review of Scalability and Efficiency Strategies

Abstract

In distributed computing, hybrid cloud-fog architectures have become a revolutionary concept for tackling the pressing issues of latency, scalability, and energy efficiency. These systems allow real-time data processing closer to end users by fusing the localized capabilities of fog computing with the centralized capacity of cloud computing. This makes them especially useful for latency-sensitive applications like smart cities, healthcare, and the Internet of Things. The technological developments, application areas, and difficulties related to hybrid systems are all examined in this study's methodical analysis of the body of existing research. With a focus on utilizing technologies like SDN, NFV, and AI-driven optimization frameworks, key focus areas include resource management, dynamic job allocation, privacy-preserving procedures, and scaling tactics. Although hybrid designs show great promise for increasing system responsiveness and efficiency, unresolved problems including resource allocation complexity, privacy concerns, and interoperability underscore the need for more study.  This work offers actionable recommendations to address these gaps, including standardization of communication protocols, integration of advanced AI techniques, and the development of energy-efficient designs. The findings lay a strong foundation for advancing hybrid cloud-fog systems and ensuring their broader adoption across diverse industries.