Optimal Cloud Computing Resource Allocation for Demand Side Management

Cao, Zijian; Lin, Jin; Wan, Can; Song, Yonghua; Zhang, Yi; Wang, Xiaohui

doi:10.1109/tsg.2015.2512712

Cited by 84 publications

(61 citation statements)

References 39 publications

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“…In order to solve this problem, cloud computing is a good solution to host SCADA systems. Cloud computing enables on-demand access to a shared pool of computing resources, such as storage, computation, network, applications, servers and services [174]- [176].…”

Section: A Need Of Big Data Management In Iot-aided Sg Systemsmentioning

confidence: 99%

Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

et al. 2019

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Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoTaided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems. energy in accordance with the needs of the consumers within the constraints of power systems. Nearly, all the generation, transmission and distribution of electrical energy is owned by the utility companies who provide electrical energy to consumers and bill them accordingly to recover their costs and earn profit.The traditional power grid worked very well from its inception in 1870 until 1970 [1]. Even though the consumers' demand for energy grew exponentially, it was still rather predictable. However, there has been a dramatic change in the nature of electrical energy consumption since 1970, as the load of electronic devices has become the fastest growing element of the total electricity demand and new sources of high electricity consumption have been developed, such as Electric Vehicles (EVs). The power grids endure a significant wastage of energy due to a number of factors, such as consumers' inefficient appliances and lack of smart technology, inefficient routing and dispensation of electrical energy, unreliable communication and monitoring, and most importantly, lack of a mechanism to store the generated electrical energy [2]- [4]. Furthermore, power grids face some other challenges as well, including growing energy demand, reliability, security, emerging renewable energy sources and aging infrastructure problems to name a few.In order to solve these challenges, the Smart Grid (SG) paradigm has appeared as a promising solution with a variety of information and communication technologies. Such technologies can improve the effectiveness, efficiency, reliability, security, sustainability, stability and scalability of the traditional power grid [5]. SG differs from traditional power grids in many aspects. For instance, SG offers a bi-directional communicat...

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Section: A Need Of Big Data Management In Iot-aided Sg Systemsmentioning

confidence: 99%

Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

et al. 2019

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“…However, error messages on the communication network and locally solving the optimization are challenges for efficient power management. The authors of [19] propose a cloud-based centralized DSM. To overcome the delay issues of the cloud, the authors introduced a two-tier cloud-based model: edge cloud, near the end users, and centralized cloud, for all edge clouds.…”

Section: Problem Statementmentioning

confidence: 99%

“…The authors in [20] propose he efficient resource allocation algorithms to enhance the performance of cloud computing for DSM. Load traffic on cloud-based DSM and network delay are the challenge for DSM in SG [19,21,22]. To overcome the delay issues, the authors of [23] propose a fog-based architecture for DSM.…”

Section: Problem Statementmentioning

confidence: 99%

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

et al. 2018

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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.

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“…The matter of resource allocation is quite expensive and desires some assumptions along with a set of workflow tasks for resource requirements, set of working servers, task consolidation meeting service level agreement, and reduction in power wastage and resource usage costs. The resource allocation inconvenience involves the acceptable provisioning and economical use available resources for applications the resource allocation drawback square measure to boot strictly due to the workloads that cause utterly totally unusual resource demands from service suppliers for the cloud [15].…”

Section: Related Workmentioning

confidence: 99%

Energy Saving from Cloud Resourcesfor a Sustainable Green Cloud ComputingEnvironment

Karuppasamy

Balakannan

2018

JCSM

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Cloud computing services are proliferation. The Cloud computing resources face major pitfall in energy consumes. The prime of energy consumption in cloud computing is by means of client computational devices, server computational devices, network computational devices and power required to cool the IT load. The cloud resources contribute high operational energy cost and emit more carbon emission to the environment. Therefore the cloud services providers need green cloud environment resolution to decrease the operational energy cost along with environmental impact. The most important objective of this effort is to trim down the energy from utilized and unutilized (idle) cloud resources and save the energy in cloud resources efficiently. To achieve the sustainable green cloud environment from an Energy Saving Algorithm used to choose the appropriate virtual services so that the power at the client, server, and network recourses can be reduced.

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Optimal Cloud Computing Resource Allocation for Demand Side Management

Cited by 84 publications

References 39 publications

Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

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

Energy Saving from Cloud Resourcesfor a Sustainable Green Cloud ComputingEnvironment

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