Fog computing is a paradigm that extends cloud computing capabilities to the network edge. Its foundations lie in understanding the theoretical principles and key concepts driving its development. This includes exploring architectural aspects which facilitate efficient and distributed data processing at the network edge. It involves examining the device and hardware support required for seamless fog computing operations, including edge devices, fog nodes, and network infrastructure. Different deployment approaches such as centralized, distributed, and hierarchical deployments bring their own advantages, limitations, and considerations. Evaluating these strategies and associated metrics aids in informed decision-making for fog infrastructure deployment. Understanding these foundations is crucial for building efficient, scalable, and resource-rich fog computing systems that address challenges in modern computing. This chapter investigates these concepts and highlights the significance of fog computing in enabling low-latency and resource-rich processing in smart infrastructure setups.
Top1. Introduction
In today's rapidly evolving technological landscape, where the Internet of Things (IoT) is revolutionizing the way we interact with the digital world, traditional cloud computing paradigms are facing significant challenges. The exponential growth of IoT devices, the increasing demand for real-time and low-latency applications, and the massive amounts of data generated at the network edge have prompted the need for a new computing paradigm that can address these unique requirements. The proliferation of the Internet of Things (IoT) has led to the deployment of a large number of sensors, enabling the creation of numerous smart infrastructure setups. The concept of cloud computing has played a crucial role in managing vast amounts of big and fast streaming data.
Fog computing has emerged as a promising solution to bridge the gap between IoT devices and the cloud, enabling efficient and decentralized processing and storage capabilities at the network edge. Fog computing extends cloud computing capabilities to the edge of the network, closer to the data source and end-users. It brings computation, storage, and networking resources closer to the data generation points, reducing the latency and bandwidth constraints associated with cloud-centric architectures. By leveraging the distributed computing power of edge devices, fog computing enables real-time data analysis, faster response times, and enhanced user experiences for a wide range of applications, including smart cities, industrial automation, healthcare, transportation, and more (Varshney & Simmhan, 2017).
Fog computing is defined as a distributed computing architecture that profoundly extends the services provided by cloud computing to the edge of the network (Dastjerdi et al., 2016). To support fundamental network operations and applications, users can rent out a vast collection of heterogeneous and decentralized devices that communicate with each other to store data and process tasks. Figure 1 shows the Cisco invented Fog computing concept, which extends and moves the Cloud platform closer to the end-user's device (Khan et al., 2017).
Figure 1. (Khan et al., 2017) At its core, fog computing aims to address the limitations of traditional cloud computing models by pushing intelligence, processing, and storage closer to the edge. This paradigm shift brings numerous benefits, such as reduced network congestion, improved data privacy and security, enhanced scalability, and the ability to operate in disconnected or intermittently connected environments. By utilizing edge devices such as routers, gateways, edge servers, and IoT devices, fog computing enables efficient data processing, analytics, and decision-making, leading to improved operational efficiency and better utilization of network resources. Moreover, fog computing introduces a collaborative and distributed approach to data processing. Instead of relying solely on centralized cloud infrastructures, fog computing encourages peer-to-peer communication and collaboration among edge devices. This enables efficient sharing of data, task offloading, and cooperative processing, allowing edge devices to leverage their collective capabilities and enhance the overall system performance.
Despite its immense potential, fog computing also presents unique challenges. These challenges include resource constraints of edge devices, ensuring data security and privacy in decentralized environments, managing the heterogeneity and scale of edge deployments, and seamlessly integrating fog computing with existing cloud infrastructures (Alzoubi et al., 2022). Overcoming these challenges requires innovative research, development of robust algorithms, and the establishment of industry standards.