Модели оценки эффективности облачных технологий и туманных вычислений
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Models of evaluations of the cloud technology and fog computing

idFaizullin R.V., Hering S.,  Vasilenko K.A. 

UDC 004.77:004.424
DOI: 10.26102/2310-6018/2020.28.1.0

  • Abstract
  • List of references
  • About authors

This article explores key approaches to industrial automation based on cloud- computing. The relevance of the study is related to the development of cloud technologies of industrial automation with elements of foggy computing, the essence of which is the ability to manage the industrial process from the cloud. In literature, this approach is called "Internet of Everything." Technology for business organization, industrial production and control of smart devices at the level of everyday life is developing rapidly. The benefits of these technologies are obvious, but the cost of use is high, provided that the service owners are external providers. There is a need to calculate the computational cost and cost of renting services. The article presents an approach to calculating the computational cost of using foggy computing on a calculated example. The simulation is based on CloudSim. In the simulation environment, the FSMRA (Fog Stable Matching Resource Allocation) algorithm has been implemented, and algorithm-based calculations can be used in the decision-making tasks for the use of cloud services and foggy computing when automating industrial objects based on use a large number of sensors and end devices in real time. Simulation results and computational cost calculations show where combinations of different technologies can maximize benefits from cloud and foggy computing.

1. Cloud technologies migrate to the clouds of Rational Enterprise Management No.4. 2014 (electronic resource) http://www.remmag.ru/upload_data/files/04-2014/Prosoft.pdf (reference date 10.01.2020)

2. Fajzullin R.V., Hering SH. [Trends in introducing the concept of “Internet of things” for production automation]. Social'no-ekonomicheskoe upravlenie: teoriya i praktika. 2018;(4):154-157. (in Russ.).

3. Maksimov K. V. Effektivnost' ispol'zovaniya oblachnykh tekhnologiy: modeli i metody otsenki \\ K. V.Maksimov \ Prikladnaya informatika \ Journal of applied informatics – Vol.11. №1(61). 2016.

4. Fajzullin R.V., Hering SH. [The Model of Data Aggregation from Clustered Devices in the Internet of Things]. Intellektual'nye sistemy v proizvodstve. 2019;17(4):156-162. DOI: 10.22213/2410-9304-2019-4-156-162

5. Danzig J.. Linear programming, its applications and generalizations. Progress Publishing House Moscow 1966

6. Wireless Communications and Mobile Computing Volume 2018, Article ID 6421607. https://doi.org/10.1155/2018/6421607

7. D. Gale, L. S. Shapley.College admissions and the stability of marriage. The American Mathematical Monthly. 1962;69:9–15. DOI: 10.1080/00029890.1962.11989827

8. N. Bessis , C. Dobre (eds.), Big Data and Internet of Things: A Roadmap for Smart Environments, Studies in Computational Intelligence. Springer International Publishing Switzerland. 2014;546:169–186. DOI: 10.1007/978-3-319-05029-4_7

9. Bellavista, P.; Zanni, A. Feasibility of fog computing deployment based on docker containerization over Raspberrypi. In Proceedings of the 18th International Conference on Distributed Computing and Networking, Hyderabad, India, 5–7 January 2017:16.

10. SuperWits Academy: CloudSim Simulation Framework Course. https://www.superwits.com/library/cloudsim-simulation-framework

11. Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience. 2011;41(1):23-50.

12. Abedin S.F., Alam M.G.R., Kazmi S.A., Tran N.H., Niyato D., Hong C.S. Resource allocation for ultra-reliable and enhanced mobile broadband IoT applications in fog network. IEEE Trans. Commun. 2019;67:489-502.

13. Yuan Y, Xu H, Wang B, Yao X. A new dominance relation-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation. 2016;20(1):16-37. DOI: 10.1109/TEVC.2015.2420112

14. Jeyakrishnan V, Sengottuvelan P., A Hybrid Strategy for Resource Allocation and Load Balancing in Virtualized Data Centers Using BSO Algorithms. Wireless Personal Communications. 2017;94( 4):2363-2375.

Faizullin Rinat Vasilovish
Candidate of Economic Sciences
Email: rf85@mail.ru

ORCID |

Kalashnikov Izhevsk State Technical University

Izhevsk, Russian Federation

Hering Stefan

Kalashnikov Izhevsk State Technical University, Izhevsk

Izhevsk, Russian Federation

Vasilenko Konstantin Alexandrovich

Email: k2857@mail.ru

Vladivostok State University Of Economics And Service

Vladivostok, Russian Federation

Keywords: industrial automation, cloud technology, foggy computing, fsmra algorithm

For citation: Faizullin R.V., Hering S., Vasilenko K.A. Models of evaluations of the cloud technology and fog computing. Modeling, Optimization and Information Technology. 2020;8(1). URL: https://moit.vivt.ru/wp-content/uploads/2020/02/FaizullinSoavtors_1_20_1.pdf DOI: 10.26102/2310-6018/2020.28.1.0 (In Russ).

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Published 31.03.2020