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Energies 2018, 11(12), 3345; https://doi.org/10.3390/en11123345

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

1
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2
CIS, Higher Colleges of Technology, Fujairah 4114, UAE
3
Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, Korea
4
Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan
5
Faculty of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan
*
Author to whom correspondence should be addressed.
Received: 3 November 2018 / Revised: 22 November 2018 / Accepted: 26 November 2018 / Published: 30 November 2018
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
Full-Text   |   PDF [603 KB, uploaded 30 November 2018]   |  

Abstract

The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts. View Full-Text
Keywords: response time; processing time; microgrid; recurring cost; data-driven smart grid; resource allocation; residential buildings; energy management; demand side; cloud-fog based smart grid response time; processing time; microgrid; recurring cost; data-driven smart grid; resource allocation; residential buildings; energy management; demand side; cloud-fog based smart grid
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bukhsh, R.; Javaid, N.; Ali Khan, Z.; Ishmanov, F.; Afzal, M.K.; Wadud, Z. Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid. Energies 2018, 11, 3345.

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