A Comprehensive Survey on Multi-Facet Fog-Computing Resource Management Techniques, Trends, Applications and Future Directions

Abstract

Due to the recent advancements in high-speed networks, underlying hardware computing resources and resource scheduling algorithms, Cloud computing has emerged as a popular computing paradigm globally providing end-user services such as infrastructure, hardware platforms and application tools. Subsequently, the researchers across various domains have integrated different services to facilitate the end users. However, the real issue faced by the cloud infrastructure is the network latency due to the physical dispersion between clients and cloud data centers. According to an estimate, billions of internet of things (IoT) devices are sharing approximately two exabytes of data daily. Such a huge amount of data can affect network performance if the underlying physical system does not expand up to the required levels, leading to performance degradation. To overcome these issues, a new computing paradigm called Fog Computing has emerged in recent years. In this paper, we discuss the recent developments in fog computing with the integration of real-time Healthcare 5.0 technology. Furthermore, we describe the proposed layered architecture and taxonomy of resource management (RM) techniques in fog computing, which consists of energy awareness, scheduling, reliability and scalability. Besides that, our survey covers the three-tier layered architecture, evaluation metrics, real-time application aspects of fog computing and tools providing the implementation of RM techniques in fog computing. Furthermore, the proposed layered architecture of the standard fog framework and different state-of-the-art techniques for utilising the computing resources of fog networks have been covered in this study. Moreover, we include various sensors to demonstrate the fog data offloading example in healthcare 5.0 applications. We also present a thorough discussion on various current and future real-time applications of fog computing. Finally, open challenges and promising future research directions have been identified and discussed in the area of fog-based real-time applications.