Simulation modeling of cloud computing for smart grid using CloudSim

Mehmi, Sandeep; Verma, Harsh Kumar; Sangal, Amrit Lal

doi:10.1016/j.jesit.2016.10.004

Cited by 42 publications

(18 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The broker is a mediator between the users and cloud service providers. It maps the requests of users to the appropriate provider such that it insures the achievement of the Quality of Service (QoS) requirements [18].…”

Section: Experimental Methodologymentioning

confidence: 99%

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

Almezeini¹,

Hafez²

2017

ijacsa

View full text Add to dashboard Cite

Section: Experimental Methodologymentioning

confidence: 99%

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

Almezeini¹,

Hafez²

2017

ijacsa

View full text Add to dashboard Cite

“…A more advanced technique of the cloud platform possesses not only big data storage but also massive computation power, which allows complex data analytics for smart grid data streaming, processing, analyzing, and storage. Readers are encouraged to read the following articles [24][25][26][27].…”

Section: Trends and Future Developmentmentioning

confidence: 99%

The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Lytras

Chui

2019

Energies

View full text Add to dashboard Cite

Human beings share the same community in which the usage of energy by fossil fuels leads to deterioration in the environment, typically global warming. When the temperature rises to the critical point and triggers the continual melting of permafrost, it can wreak havoc on the life of animals and humans. Solutions could include optimizing existing devices, systems, and platforms, as well as utilizing green energy as a replacement of non-renewable energy. In this special issue "Artificial Intelligence for Smart and Sustainable Energy Systems and Applications", eleven (11) papers, including one review article, have been published as examples of recent developments. Guest editors also highlight other hot topics beyond the coverage of the published articles.

show abstract

“…However, hope bifurcation of economic systems is dependent on several other rules. In [39], the authors have proposed a cloud computing simulation model of the smart grid for hosting smart grid applications, determining VM/ cloudlet parameters, along with cost prediction and processing time for the smart grid tasks. However, security policies for smart grid cloud are ignored.…”

Section: Related Workmentioning

confidence: 99%

Evaluation and Analysis of Bio-Inspired Optimization Techniques for Bill Estimation in Fog Computing

Arshad¹,

Khattak²,

Shah³

et al. 2018

ijacsa

View full text Add to dashboard Cite

In light of constant developments in the realm of Information Communication and Technologies, large-scale businesses and Internet service providers have realized the limitation of data storage capacity available to them. This led organizations to cloud computing, a concept of sharing of resources among different service providers by renting these resources through service level agreements. Fog computing is an extension to cloud computing architecture in which resources are brought closer to the consumers. Fog computing, being a distinct from cloud computing as it provides storage services along with computing resources. To use these services, the organizations have to pay according to their usage. In this paper, two nature-inspired algorithms, i.e. Pigeon Inspired Optimization (PIO) and Binary Bat Algorithm (BBA) are compared to regulate the effective management of resources so that the cost of resources can be curtailed and billing can be achieved by calculating utilized resources under the service level agreement. PIO and BBA are used to evaluate energy utilization by cloudlets or edge nodes that can be used subsequently for approximating the utilization and bill through a Time of Use pricing scheme. We appraise abovementioned techniques to evaluate their performance concerning the bill estimation based on the usage of fog servers. With respect to the utilization of resources and reduction in the bill, simulation results have revealed that the BBA gives pointedly better results than PIO.

show abstract

Simulation modeling of cloud computing for smart grid using CloudSim

Cited by 42 publications

References 8 publications

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Evaluation and Analysis of Bio-Inspired Optimization Techniques for Bill Estimation in Fog Computing

Contact Info

Product

Resources

About