Efficient Resource Allocation Model for Residential Buildings in Smart Grid Using Fog and Cloud Computing

Fatima, Aisha; Javaid, Nadeem; Waheed, Momina; Nazar, Tooba; Shabbir, Shaista; Sultana, Tanzeela

doi:10.1007/978-3-319-93554-6_26

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…The cost depends on two things: how much it costs to move data and how much it costs to run a virtual machine. We have calculated different costs associated with the execution of the job [ 45 , 46 ]. These costs are VM cost (VM cost ), the data transmission cost (DT cost ), and the MG cost (MG cost ) within the framework of the model that was presented.…”

Section: Performance Analysis and Discussionmentioning

confidence: 99%

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Singhal

Athithan

Alomar

et al. 2023

Sensors

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Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

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Section: Performance Analysis and Discussionmentioning

confidence: 99%

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Singhal

Athithan

Alomar

et al. 2023

Sensors

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“…classified into 6 regions [27][28][29][30][31][32][33][34][35][36]. In this research, we consider region 0, which is North America, because the percentage of users in this region is 80 million [31].…”

Section: Firefly Optimization Approachmentioning

confidence: 99%

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Wang

2022

Mathematical Problems in Engineering

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As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time.

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“…Fatima et al [101] proposed a fog computing based model for resource allocation in residential buildings. The performance has been measured using response time and processing time.…”

Section: Edge Computing and Fog Computing-based Techniques In Smart Hmentioning

confidence: 99%

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

et al. 2019

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In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

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Efficient Resource Allocation Model for Residential Buildings in Smart Grid Using Fog and Cloud Computing

Cited by 7 publications

References 12 publications

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

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