Towards an Elastic Fog‐Computing Framework for IoT Big Data Analytics Applications

Abstract

IoT applications have been being moved to the cloud during the last decade in order to reduce operating costs and provide more scalable services to users. However, IoT latency-sensitive big data streaming systems (e.g., smart home application) is not suitable with the cloud and needs another model to fit in. Fog computing, aiming at bringing computation, communication, and storage resources from “cloud to ground” closest to smart end-devices, seems to be a complementary appropriate proposal for such type of application. Although there are various research efforts and solutions for deploying and conducting elasticity of IoT big data analytics applications on the cloud, similar work on fog computing is not many. This article firstly introduces AutoFog, a fog-computing framework, which provides holistic deployment and an elasticity solution for fog-based IoT big data analytics applications including a novel mechanism for elasticity provision. Secondly, the article also points out requirements that a framework of IoT big data analytics application on fog environment should support. Finally, through a realistic smart home use case, extensive experiments were conducted to validate typical aspects of our proposed framework.

Modeling of a Generic Edge Computing Application Design

“…Hence, a fog infrastructure may be considered as an integration of some capabilities coming down from the cloud and other capabilities coming up from IoT devices, thus getting better performance and efficiency [52]. In this context, the whole fog ecosystem may be seen as composed by three strata, such as edge devices, fog, and cloud, allowing the components of each fog application to be deployed in the most convenient stratum, related to the type of task run by each component or the latency rate to be expected [53].…”