Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework†
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Department of Computer System Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
College of Computer and Information Systems, Al Yamamah University, Riyadh 11512, Saudi Arabia
Department of Electrical Engineering, Institute of Space Technology (IST), Islamabad 44000, Pakistan
Author to whom correspondence should be addressed.
This paper is an extended version of paper accepted in 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018), 2018 in Cyprus.
Energies 2019, 12(5), 815; https://doi.org/10.3390/en12050815
Received: 29 January 2019 / Revised: 18 February 2019 / Accepted: 21 February 2019 / Published: 1 March 2019
(This article belongs to the Special Issue Demand-Response in Smart Buildings)
In this work, a new orchestration of Consumer to Fog to Cloud (C2F2C) based framework is proposed for efficiently managing the resources in residential buildings. C2F2C is a three layered framework consisting of cloud layer, fog layer and consumer layer. Cloud layer deals with on-demand delivery of the consumer’s demands. Resource management is intelligently done through the fog layer because it reduces the latency and enhances the reliability of cloud. Consumer layer is based on the residential users and their electricity demands from the six regions of the world. These regions are categorized on the bases of the continents. Two control parameters are considered: clusters of buildings and load requests, whereas four performance parameters are considered: Request Per Hour (RPH), Response Time (RT), Processing Time (PT) and cost in terms of Virtual Machines (VMs), Microgrids (MGs) and data transfer. These parameters are analysed by the round robin algorithm, equally spread current execution algorithm and our proposed algorithm shortest job first. Two scenarios are used in the simulations: resource allocation using MGs and resource allocation using MGs and power storage devices for checking the effectiveness of the proposed work. The simulation results of the proposed technique show that it has outperformed the previous techniques in terms of the above-mentioned parameters. There exists a tradeoff in the PT and RT as compared to cost of VM, MG and data transfer.