S. P. Vorobyev, S. N. Shirobokova, V. A. Evsin Exchange Model of a Distributed Registry System for Cloud, Fog and Edge Computing
S. P. Vorobyev, S. N. Shirobokova, V. A. Evsin Exchange Model of a Distributed Registry System for Cloud, Fog and Edge Computing

In this paper describes aspects that should be consider when building a model for optimizing the architecture of a distributed registry (the dynamic nature of the location of network services, the generation of explosive volumes of service network traffic, the difficult-to-predict nature of heterogeneous information traffic of various IoT devices, the presence of rather complex specific intensive network interconnections and data exchange interactions when synchronizing new blocks or records, achieving cryptographic consensus and backup of full-fledged copies of the registry, the type of consensus protocol, the role of a distributed registry participant) using the concept of cloud, fog and edge computing and Internet of Things technology. The issues of the necessity of modeling information traffic in a distributed registry network as fractal are considered. A formalized formulation of the problem of minimizing information traffic and network load is presented, taking into account the  implementation of services in the environment of cloud, fog and boundary computing within the framework of building an optimal multilevel topology of the distributed registry system computing network architecture.


multilevel topology, distributed registry, cloud computing, fog computing, edge computing.

PP. 11-21.

DOI 10.14357/20718632220202

1. Federal'nyj Zakon ot 31 iyulya 2020 g. n 259-FZ "O cifrovyh finanso-vyh aktivah, cifrovoj valyute i o vnesenii izmenenij v otdel'nye zako-nodatel'nye akty Rossijskoj Federacii" [Federal Law No. 259-FZ of July 31, 2020 "On Digital Financial Assets, Digital Currency and on Amendments to Certain Legislative Acts of the Russian Federation".]. Available at: (Accessed: 15.01.2022).
2. Jiang F., Zhang Y. 2022. An Integrated Impact of Blockchain Technology on Suppy Chain Management and the Logistics Industry // Handbook of Research on Social Impacts of E-Payment and Blockchain Technology. 29 p. DOI: 10.4018/978-1-7998-9035-5.ch010
3. Gong J., Navimipour N.J. An in-depth and systematic literature review on the blockchain-based approaches for cloud computing // Cluster Computing. 2021. DOI:10.1007/s10586-021-03412-2.
4. Roig P.J., Alcaraz S., Gilly K., Bernad C., Juiz C. 2016. Modeling of a Generic Edge Computing Application Design // Sensors (Basel, Switzerland), no. 21, 2021. DOI:10.3390/s21217276.
5. Vorobyev S.P. 2016. The mathematical model of building a multi-level topology of computer network for distributed corporate system based on the inverse problem // Journal of Engineering and Applied Sciences. vol. 11. is 6. pp. 1243-1247.
6. Vorobyev S.P. 2009. Vozmozhnye napravlenija ispol'zovanija koncepcii mnogourovnevoj topologii i optimizacii raspredelennyh korporativnyh sistem [Possible Directions of Application of Multi-Layer Topology Concept in Designing and Optimization of Distributed  Corporate Systems]. Voprosy sovremennoj nauki i praktiki [Problems of Contemporary Science and Practice]. Vernadsky University. №8. pp. 131-143.
7. Prince Sekwatlakwatla, Maredi Mphahlele, Tranos Zuva. 2016. Traffic flow prediction in cloud computing/ 2016 International Conference on Advances in Computing and Communication Engineering (ICACCE) 28-29 Nov. DOI: 10.1109/ICACCE.2016.8073735
8. Arwa M., Hamdan M., Khan S., Abdelaziz A., Babiker S.F., Imran M., Marsono M.N. 2021. Software-defined networks for resource allocation in cloud computing: A survey. Comput. Netw. 195. 108151.
9. Samouylov K.E., Samouylov I.A., Buzhin I.G., Mironov Y.B. 2018. Model' funkcionirovanija telekommunikacionnogo oborudovanija program-mnokonfiguriruemyh setej [Model of Functioning of Telecommunication Equipment for Softwareconfigurated Networks] // Sovremennye informacionnye tehnologii i IT-obrazovanie [Modern information technologies and IT education] , vol. 14, no. 1, pp. 13-26.
10. Liu Q., Zou X. 2019. Research on trust mechanism of cooperation innovation with big data processing based on blockchain. EURASIP Journal on Wireless Communications and Networking, no. 26, 2019. DOI: 10.1186/s13638-019-1340-5.
11. Trenogin N.G., Petrov M.N., Sokolov D.E. 2017. Svojstva fraktal'nogo trafika pri prohozhdenii sistemy massovogo obsluzhivanija s ochered'ju [Properties of fractal traffic on the output of a queuing system] // Sibirskij ajerokosmicheskij zhurnal [The Siberian Aerospace Journal]. vol. 18. no. 1. pp. 105-110.
12. Porshnev S.V., Bozhalkin D.A. 2016. K voprosu o samopodobii trafika, peredavaemogo v magistral'nom internet-kanale [On the question of self-similarity of the traffic transmitted in the backbone internet channel] // Fundamental'nye issledovanija [Fundamental research]. № 4-2. pp. 301-310.
13. Trenogin N.G., Petrov M.N., Sokolov D.E. 2017. "Empirical relationship for queue length estimation in a system with fractal shot input" Sibirskij ajerokosmicheskij zhurnal [The Siberian Aerospace Journal], vol. 18. no. 2. pp. 294-299.
14. Namestnikov S.M., Sluzhivyj M.N., Ukraincev J.D. 2016. Osnovy teorii teletrafika [Fundamentals of the theory of teletraphy] // Ulyanovsk, UlSTU. 154 p. (In Russ.).
15. Kurejchik V.M. 2002. Geneticheskie algoritmy i ih primenenie [Genetic algorithms and their application] / 2nd ed., exp. // Taganrog: Publishing Company TRTU. 242 p.
16. Goldberg D.E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning // Addison-Wesley Publishing Company, Inc. 412 p.
17. Maghawry A., Hodhod R.A., Omar Y.M., Kholief M.H. 2021. An approach for optimizing multi-objective problems using hybrid genetic algorithms // Soft Computing, no. 25, 389-405 pp. DOI:10.1007/s00500- 020-05149-3.
18. Fu G., Huang H., Li Y., Zhou J. 2021. An adaptive hybrid evolutionary algorithm and its application in aeroengine maintenance scheduling problem // Soft Computing, no.    25, 6527-6538 pp. DOI:10.1007/s00500-021-05647-y.
19. Machado J.G., Pires M.G., Bertoni F.C., Pimenta A.H., Camargo H.A. 2021. A Modified NSGA-DO for Solving  Multiobjective Optimization Problems // 10th Brazilian Conference on Intelligent Systems, vol. 13073, DOI:10.1007/978-3-030-91702-9_9.

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