Abstract
In this article, a resource allocation model is presented in order to optimize the resources in residential buildings. The whole world is categorized into six regions depending on its continents. The fog helps cloud computing connectivity on the edge network. It also saves data temporarily and sends to the cloud for permanent storage. Each continent has one fog which deals with three clusters having 100 buildings. Microgrids (MGs) are used for the effective electricity distribution among the consumers. The control parameters considered in this paper are: clusters, number of buildings, number of homes and load requests whereas the performance parameters are: cost, Response Time (RT) and Processing Time (PT). Particle Swarm Optimization with Simulated Annealing (PSOSA) is used for load balancing of Virtual Machines (VMs) using multiple service broker policies. Service broker policies in this paper are: new dynamic service proximity, new dynamic response time and enhanced new response time. The results of proposed service broker policies with PSOSA are compared with the existing policy: new dynamic service proximity. New dynamic response time and enhanced new dynamic response time performs better than the existing policy in terms of cost, RT and PT. However, the maximum RT and PT of proposed policies is more than the existing policy. We have used CloudAnalyst for conducting simulations for the proposed scheme.
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Fatima, A., Javaid, N., Waheed, M., Nazar, T., Shabbir, S., Sultana, T. (2019). Efficient Resource Allocation Model for Residential Buildings in Smart Grid Using Fog and Cloud Computing. In: Barolli, L., Xhafa, F., Javaid, N., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2018. Advances in Intelligent Systems and Computing, vol 773. Springer, Cham. https://doi.org/10.1007/978-3-319-93554-6_26
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DOI: https://doi.org/10.1007/978-3-319-93554-6_26
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