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Resource Provisioning in Fog Computing: From Theory to Practice

Department of Information Technology, Ghent University—imec, IDLab, Technologiepark-Zwijnaarde 126, 9052 Gent, Belgium
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This paper is an extended version of a conference paper: Towards Network-Aware Resource Provisioning in Kubernetes for Fog Computing applications. In Proceedings of the IEEE Conference on Network Softwarization, Paris, France, 24–28 June 2019.
These authors contributed equally to this work.
Sensors 2019, 19(10), 2238; https://doi.org/10.3390/s19102238
Received: 15 April 2019 / Revised: 10 May 2019 / Accepted: 12 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Edge/Fog/Cloud Computing in the Internet of Things)
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Abstract

The Internet-of-Things (IoT) and Smart Cities continue to expand at enormous rates. Centralized Cloud architectures cannot sustain the requirements imposed by IoT services. Enormous traffic demands and low latency constraints are among the strictest requirements, making cloud solutions impractical. As an answer, Fog Computing has been introduced to tackle this trend. However, only theoretical foundations have been established and the acceptance of its concepts is still in its early stages. Intelligent allocation decisions would provide proper resource provisioning in Fog environments. In this article, a Fog architecture based on Kubernetes, an open source container orchestration platform, is proposed to solve this challenge. Additionally, a network-aware scheduling approach for container-based applications in Smart City deployments has been implemented as an extension to the default scheduling mechanism available in Kubernetes. Last but not least, an optimization formulation for the IoT service problem has been validated as a container-based application in Kubernetes showing the full applicability of theoretical approaches in practical service deployments. Evaluations have been performed to compare the proposed approaches with the Kubernetes standard scheduling feature. Results show that the proposed approaches achieve reductions of 70% in terms of network latency when compared to the default scheduling mechanism. View Full-Text
Keywords: smart cities; IoT; fog computing; resource provisioning; Kubernetes smart cities; IoT; fog computing; resource provisioning; Kubernetes
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Santos, J.; Wauters, T.; Volckaert, B.; De Turck, F. Resource Provisioning in Fog Computing: From Theory to Practice . Sensors 2019, 19, 2238.

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