Fog Computing Architectures: A Reference for Practitioners

Soon after realizing that cloud computing could indeed help several industries overcome classical product-centric approaches in favor of more affordable service-oriented business models, we are witnessing the rise of a new disruptive computing paradigm, namely fog computing. Essentially, fog computing can be considered as an evolution of cloud computing, in the sense that the former extends the latter to the edge of the network (i.e., where the connected devices -- the things -- are) without discontinuity, realizing the so-called "cloud-to-thing continuum." Since its infancy, fog computing has been considered as a necessity within several Internet of Things (IoT) domains (one for all: Industrial IoT) and, more generally, wherever embedded artificial intelligence and/or more advanced distributed capabilities were required. Fog computing cannot be considered only a fancy buzzword: according to separate, authoritative analyses, its global market will reach $18 billion by 2022, while nearly 45 percent of the world's data will be moved to the network edge by 2025. In this article, we take stock of the situation, summarizing the most modern and mature fog computing initiatives from the standardization, commercial, and open source communities' perspectives.

[1]  Alan Davy,et al.  Resource Aware Placement of Data Analytics Platform in Fog Computing , 2016, Cloud Forward.

[2]  Rajkumar Buyya,et al.  Fog Computing: Principles, Architectures, and Applications , 2016, ArXiv.

[3]  Muhammad Aamir Nadeem,et al.  Fog computing: An emerging paradigm , 2016, 2016 Sixth International Conference on Innovative Computing Technology (INTECH).

[4]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[5]  Eui-nam Huh,et al.  Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.

[6]  Hamid Reza Arkian,et al.  MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications , 2017, J. Netw. Comput. Appl..

[7]  Cesare Pautasso,et al.  Microservices in Practice, Part 1: Reality Check and Service Design , 2017, IEEE Software.

[8]  Prem Prakash Jayaraman,et al.  Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions , 2018, IEEE Access.

[9]  John Krogstie,et al.  F2c2C-DM: A Fog-to-cloudlet-to-Cloud Data Management Architecture in Smart City , 2019, 2019 IEEE 5th World Forum on Internet of Things (WF-IoT).