scholarly journals Resource Allocation on a Hybrid Cloud for Smart Grids

2016 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Alan Briones ◽  
Ramon Martín de Pozuelo ◽  
Joan Navarro ◽  
Agustín Zaballos
Keyword(s):  
Resource Allocation ◽  
Smart Grid ◽  
Smart Grids ◽  
Virtual Machines ◽  
Operating Costs ◽  
Hybrid Cloud ◽  
Hybrid Clouds ◽  
To Come ◽  
Resource Allocation Scheme ◽  
Multi Cloud

The use of hybrid clouds enables companies to cover their demands of IT resources saving costs and gaining flexibility in the deployment of infrastructures by paying under demand these resources. However, considering a scenario with various services to be allocated in more than one cloud, it is necessary to find the distribution of services that minimizes the overall operating costs. This paper researches on the resource allocation methodology to be applied in a multi-cloud scenario based on the findings derived from the framework used for the FINESCE project. The purpose of this work is to define a methodology to assist on the hybrid cloud selection and configuration in the Smart Grid for both generic and highly-constrained scenarios in terms of latency and availability. Specifically, the presented method is aimed to determine which is the best cloud to allocate a resource by (1) optimizing the system with the information of the network and (2) minimizing the occurrence of collapsed or underused virtual machines. Also, to assess the performance of this method and any alternative proposals, a general set of metrics has been defined. These metrics have been refined taking into account the expertise of FINESCE partners in order to shape Smart Grid clouds and reduce the complexity of computation. Finally, using the data extracted from the FINESCE testbed, a decision tree is used to come up with the best resource allocation scheme.

scholarly journals Optimal Resource Allocation For QOS Supports In Smart Grid Neighborhood Area Network

2021 ◽  
Author(s):  
Mohammad S. Yazdi
Keyword(s):  
Resource Allocation ◽  
Smart Grid ◽  
Communication Networks ◽  
Area Network ◽  
Allocation Scheme ◽  
Optimal Resource Allocation ◽  
Optimal Resource ◽  
Resource Allocation Scheme ◽  
Smart Grid Communication ◽  
Neighborhood Area Network

Smart grid is a utility network, with advanced information and communications technologies for improved control, efficiency, reliability and safety in electric power distribution and management. Smart grid communication network consists of three interconnected communication networks: home area network (HAN), neighborhood area network (NAN), and wide area network (WAN). Our thesis is focused on NAN. The information flow in smart grid communication networks has different Quality of Service (QoS) requirements in terms of packet loss rate, throughput, and latency. By deploying QoS mechanisms, we can get the real time feedbacks which can be used to supply electricity based on need, thus reducing the wastage of electricity. First, we conducted Opnet simulations for NAN. We evaluated two technologies, Zigbee and wireless local area network (WLAN), for NAN. The simulation results demonstrate that latency can be reduced for the data flow with a higher priority with an appropriate QoS mechanism. Next, we proposed an optimal resource allocation scheme to reduce delay and provide differentiated services, in terms of latency, to different classes of traffic in the NAN. The problem is formulated into a linear programming (LP) problem, which can be solved efficiently. The simulation results and comparison demonstrates that the proposed resource allocation scheme can provide overall lower latency of the various data flows. Our method also lowers the delay of the data flow with a higher priority.

scholarly journals Optimal Resource Allocation For QOS Supports In Smart Grid Neighborhood Area Network

2021 ◽  
Author(s):  
Mohammad S. Yazdi
Keyword(s):  
Resource Allocation ◽  
Smart Grid ◽  
Communication Networks ◽  
Area Network ◽  
Allocation Scheme ◽  
Optimal Resource Allocation ◽  
Optimal Resource ◽  
Resource Allocation Scheme ◽  
Smart Grid Communication ◽  
Neighborhood Area Network

Smart grid is a utility network, with advanced information and communications technologies for improved control, efficiency, reliability and safety in electric power distribution and management. Smart grid communication network consists of three interconnected communication networks: home area network (HAN), neighborhood area network (NAN), and wide area network (WAN). Our thesis is focused on NAN. The information flow in smart grid communication networks has different Quality of Service (QoS) requirements in terms of packet loss rate, throughput, and latency. By deploying QoS mechanisms, we can get the real time feedbacks which can be used to supply electricity based on need, thus reducing the wastage of electricity. First, we conducted Opnet simulations for NAN. We evaluated two technologies, Zigbee and wireless local area network (WLAN), for NAN. The simulation results demonstrate that latency can be reduced for the data flow with a higher priority with an appropriate QoS mechanism. Next, we proposed an optimal resource allocation scheme to reduce delay and provide differentiated services, in terms of latency, to different classes of traffic in the NAN. The problem is formulated into a linear programming (LP) problem, which can be solved efficiently. The simulation results and comparison demonstrates that the proposed resource allocation scheme can provide overall lower latency of the various data flows. Our method also lowers the delay of the data flow with a higher priority.

Communication Aspects of Resource Management in Hybrid Clouds

2015 ◽  
pp. 1827-1851
Author(s):  
Luiz F. Bittencourt ◽  
Edmundo R. M. Madeira ◽  
Nelson L. S. da Fonseca
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Resource Management ◽  
Hybrid Cloud ◽  
Public Cloud ◽  
Hybrid Clouds ◽  
Cloud Management ◽  
Private And Public ◽  
Cloud Resources

Organizations owning a datacenter and leasing resources from public clouds need to efficiently manage this heterogeneous infrastructure. In order to do that, automatic management of processing, storage, and networking is desirable to support the use of both private and public cloud resources at the same time, composing the so-called hybrid cloud. In this chapter, the authors introduce the hybrid cloud concept and several management components needed to manage this infrastructure. They depict the network as a fundamental component to provide quality of service, discussing its influence in the hybrid cloud management and resource allocation. Moreover, the authors present the uncertainty in the network channels as a problem to be tackled to avoid application delays and unexpected costs from the leasing of public cloud resources. Challenging issues in the hybrid cloud management is the last topic of this chapter before the concluding remarks.

scholarly journals Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7846
Author(s):  
Junaid Akram ◽  
Arsalan Tahir ◽  
Hafiz Suliman Munawar ◽  
Awais Akram ◽  
Abbas Z. Kouzani ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Smart Grid ◽  
Virtual Machines ◽  
Fog Computing ◽  
Round Robin ◽  
Ant Colony ◽  
Electrical Network ◽  
Ant Colony Optimisation ◽  
Inertia Weight

The smart grid (SG) is a contemporary electrical network that enhances the network’s performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.

Communication Aspects of Resource Management in Hybrid Clouds

2013 ◽  
pp. 409-433
Author(s):  
Luiz F. Bittencourt ◽  
Edmundo R. M. Madeira ◽  
Nelson L. S. da Fonseca
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Resource Management ◽  
Hybrid Cloud ◽  
Public Cloud ◽  
Hybrid Clouds ◽  
Cloud Management ◽  
Private And Public ◽  
Cloud Resources

Organizations owning a datacenter and leasing resources from public clouds need to efficiently manage this heterogeneous infrastructure. In order to do that, automatic management of processing, storage, and networking is desirable to support the use of both private and public cloud resources at the same time, composing the so-called hybrid cloud. In this chapter, the authors introduce the hybrid cloud concept and several management components needed to manage this infrastructure. They depict the network as a fundamental component to provide quality of service, discussing its influence in the hybrid cloud management and resource allocation. Moreover, the authors present the uncertainty in the network channels as a problem to be tackled to avoid application delays and unexpected costs from the leasing of public cloud resources. Challenging issues in the hybrid cloud management is the last topic of this chapter before the concluding remarks.

GCN based virtual resource allocation scheme for power internet of things

2021 ◽  
pp. 1-14
Author(s):  
Ling Wei ◽  
Hong-Xuan Luo ◽  
Shao-Lei Zhai ◽  
Bo-Yang Huang ◽  
Ye Chen
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Internet Of Things ◽  
Smart Grid ◽  
Power Grid ◽  
Distribution Model ◽  
Smart Devices ◽  
Time Variability ◽  
Allocation Scheme ◽  
Resource Allocation Scheme

With the construction of smart grid, increasing number of smart devices will be connected to the power communication network. Therefore, how to allocate the resources of access devices has become an urgent problem to be solved in smart grid. However, due to the diversity and time-variability of access devices at the edge of the power grid, such dynamic changes may lead to untimely and unbalanced resource allocation of the power grid and additional system overhead, resulting in reducing the efficiency of power grid operation, unbalanced workload and other problems. In this paper, a grid resource allocation scheme based on Gauss optimization is proposed. The grid virtualization application resources are managed through three main steps: decomposition, combination and exchange, so as to realize the reasonable allocation of grid resources. Considering the time-variability of the grid topology and the diversity of the access device, the computational complexity of the traditional data analysis model is too high to be suitable for time-sensitive power network structure. This paper proposes an MPNN framework combined with the Graph Convolutional Network (GCN) to enhance the calculation efficiency and realize the rapid allocation of network resources. Since the smart gateway connected by the grid terminal has certain computation ability, the cloud computing used in distribution model in deep learning to find the optimal solution can be distributed in the cloud and edge computing gateway. In this way, The entire electricity network can efficiently manage and orchestrate virtual services to maximize the utility of grid virtual resources. Furthermore, this paper also adopt the GG-NN (Gated Graph Neural Network) which is based on the MPNN framework in the training. Finally, we carry out simulation for the Gauss optimization scheme and the MPNN-based scheme to verify that the convolutional diagram neural network is suitable for virtual resource allocating in multi-access power Internet-of –Things (IoTs).

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