Theoretical modelling of fog computing: a green computing paradigm to support IoT applications

Abstract

In this study, the authors focus on theoretical modelling of the fog computing architecture and compare its performance with the traditional cloud computing model. Existing research works on fog computing have primarily focused on the principles and concepts of fog computing and its significance in the context of internet of things (IoT). This work, one of the first attempts in its domain, proposes a mathematical formulation for this new computational paradigm by defining its individual components and presents a comparative study with cloud computing in terms of service latency and energy consumption. From the performance analysis, the work establishes fog computing, in collaboration with the traditional cloud computing platform, as an efficient green computing platform to support the demands of the next generation IoT applications. Results show that for a scenario where 25% of the IoT applications demand real-time, low-latency services, the mean energy expenditure in fog computing is 40.48% less than the conventional cloud computing model. 2 Fog computing architecture As stated earlier, fog computing is a non-trivial extension of cloud computing [7], and typically serves as a platform that bridges IET Networks

QoS-Aware Deployment of IoT Applications Through the Fog

“…[14] aims at evaluating service latency and energy consumption of the new Fog paradigm applied to the IoT, as compared to traditional Cloud scenaria. The model of [14], however, deals only with the behaviour of software already deployed over Fog infrastructures.…”

“…[14] aims at evaluating service latency and energy consumption of the new Fog paradigm applied to the IoT, as compared to traditional Cloud scenaria. The model of [14], however, deals only with the behaviour of software already deployed over Fog infrastructures. [7] follows a more pragmatic approach, by proposing a C++ programming framework for the Fog that provides APIs for resource discovery and QoS-aware incremental deployment via containerisation.…”

Data Processing Delay Optimization in Mobile Edge Computing

“…Sarkar and Misra [22] established a theoretical model of fog computing' architecture and defined the structural components mathematically. Then, they compared it to the traditional architecture of cloud computing and analyzed the performance of delay and energy consumption in the background of IoT.…”

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

“…Therefore, fog nodes aid in reducing the amount of data that has to be transferred through the network and ensures that the demands of delay‐sensitive critical systems are met. Sarkar et al proved that fog computing reduces the overall energy consumption by approximately 40% when compared with that of the conventional cloud. Dense geographical distributionFog computing environments consist of a large number of geographically distributed heterogeneous nodes spanning multiple domains. The improved capabilities of the smart devices provide opportunities for processing the data in closer proximity of the end users, rather than having to send all the collected data to the distant cloud datacenters.…”