# Demystifying Fog Computing: Characterizing Architectures, Applications and Abstractions

## TL;DR

This paper provides a comprehensive review of Fog computing, detailing its architectures, applications, and abstractions, highlighting its unique capabilities and challenges in supporting IoT ecosystems.

## Contribution

It offers a systematic characterization of Fog computing's system architecture, application features, and platform abstractions, along with real-world case studies and identified challenges.

## Key findings

- Fog computing enables low-latency processing at the network edge.
- There are significant gaps between Fog computing's potential and current practical implementations.
- Key challenges include mobility, privacy, and platform sustainability.

## Abstract

Internet of Things (IoT) has accelerated the deployment of millions of sensors at the edge of the network, through Smart City infrastructure and lifestyle devices. Cloud computing platforms are often tasked with handling these large volumes and fast streams of data from the edge. Recently, Fog computing has emerged as a concept for low-latency and resource-rich processing of these observation streams, to complement Edge and Cloud computing. In this paper, we review various dimensions of system architecture, application characteristics and platform abstractions that are manifest in this Edge, Fog and Cloud eco-system. We highlight novel capabilities of the Edge and Fog layers, such as physical and application mobility, privacy sensitivity, and a nascent runtime environment. IoT application case studies based on first-hand experiences across diverse domains drive this categorization. We also highlight the gap between the potential and the reality of Fog computing, and identify challenges that need to be overcome for the solution to be sustainable. Together, our article can help platform and application developers bridge the gap that remains in making Fog computing viable.