Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (1807.00976v1)

Abstract: Emerging technologies like the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architecture and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction.

Analyzing Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions

The paper provides a comprehensive survey on Fog computing, delineating its necessity due to emerging technologies such as the Internet of Things (IoT). These technologies necessitate latency-sensitive computation for efficient real-time application processing. IoT environments generate vast datasets, often referred to as big data, traditionally processed using cloud infrastructure due to its scalability and on-demand service capabilities. However, the inherent latency in solely relying on cloud-based processing for urgent IoT applications prompts the need for Fog computing, which positions itself closer to the data source—acting as an intermediary between cloud data centers and IoT devices.

Fog Computing Overview

Fog computing is characterized as a decentralized computing paradigm offering enhanced privacy, reduced latency, and efficient resource utilization by shifting computational load onto local dedicated hardware and resource-abundant devices. It involves IoT devices performing immediate computations through interconnected Fog devices, creating an extra layer of data filtration and processing before transferring data to the cloud. The paper grounds this concept by exploring Fog’s taxonomy, resource allocation, and scheduling challenges, highlighting that these processes are still maturing, consequently necessitating further research.

Architectural Insights

The paper explores several proposed Fog architectures, evaluating their components and roles in supporting infrastructure deployment, tackling tasks spanning simple computation to complex data management. Through a three-layer high-level architecture (IoT, Fog, and Cloud), it emphasizes seamless integration, ensuring the handling of both latency-critical applications and extensive data sets. Moreover, various components such as Fog devices, servers, and gateways are considered pivotal to effective Fog architecture.

Taxonomy and Research Directions

The proposed taxonomy in the paper addresses the requirements for infrastructure, platform, and application in Fog computing. This involves exploring network and infrastructure needs to support various connected devices, the intricacies of resource management, the guarantee of Quality of Service (QoS), and privacy concerns due to data proximity. The paper identifies research gaps such as efficient resource allocation, task scheduling, and the development of effective fault tolerance strategies within Fog environments.

Strong Numerical and Conceptual Insights

An in-depth exploration of search trends and research publication data illustrates the rising academic interest in Fog computing, indicating its substantial role within the research community. Public interest marked through Google Scholar clearly demonstrates a trajectory of increasing importance. Furthermore, the discussion concludes with pertinent research directions, proposing a structured yet flexible standard Fog architecture and emphasizing the interoperability and seamless management of heterogeneous devices under varying network conditions.

Implications and Future Research

The potential for Fog computing to manage processing loads and enhance system responsiveness in latency-sensitive applications suggests profound implications for real-time IoT services, smart city infrastructures, and industry 4.0 environments. The paper’s exploration of future research emphasizes critical areas such as standardized Fog architectures, seamless device integration across networks, and robust error management systems. Deploying effective resource management strategies and leveraging microservices architectures will likely propel Fog computing from its nascent theoretical stages to a practical and effective technology in real-world applications.

In conclusion, the paper provides a foundational basis for understanding the dynamic and evolving nature of Fog computing, delineating its current achievements and setting a definitive path for future research and development. By enhancing the efficiency of IoT systems, Fog computing stands to transform computational models and considerably shape the technological landscape.