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Integration of PBFT and Raft Algorithms with Recurrent Neural Networks to Improve the Reliability of Distributed Systems. / Bogdanov, Alexander; Shchegoleva, Nadezhda; Khvatov, Valery; Kiyamov, Jasur; Dik, Gennady; Rakhmatullayev, Ilkhom; Ergashev, Shakhboz; Umurzakov, Oybek.

Computational Science and Its Applications -- ICCSA 2024 Workshops. ed. / Osvaldo Gervasi; Beniamino Murgante; Chiara Garau; David Taniar; Ana Maria A. C. Rocha; Maria Noelia Faginas Lago. Cham : Springer Nature, 2024. p. 226-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14815 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Bogdanov, A, Shchegoleva, N, Khvatov, V, Kiyamov, J, Dik, G, Rakhmatullayev, I, Ergashev, S & Umurzakov, O 2024, Integration of PBFT and Raft Algorithms with Recurrent Neural Networks to Improve the Reliability of Distributed Systems. in O Gervasi, B Murgante, C Garau, D Taniar, AMA C. Rocha & MN Faginas Lago (eds), Computational Science and Its Applications -- ICCSA 2024 Workshops. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14815 LNCS, Springer Nature, Cham, pp. 226-237, The 24th International Conference on Computational Science and Its Applications, ICCSA 2024, Ханой, Viet Nam, 1/07/24. https://doi.org/10.1007/978-3-031-65154-0_14

APA

Bogdanov, A., Shchegoleva, N., Khvatov, V., Kiyamov, J., Dik, G., Rakhmatullayev, I., Ergashev, S., & Umurzakov, O. (2024). Integration of PBFT and Raft Algorithms with Recurrent Neural Networks to Improve the Reliability of Distributed Systems. In O. Gervasi, B. Murgante, C. Garau, D. Taniar, A. M. A. C. Rocha, & M. N. Faginas Lago (Eds.), Computational Science and Its Applications -- ICCSA 2024 Workshops (pp. 226-237). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14815 LNCS). Springer Nature. https://doi.org/10.1007/978-3-031-65154-0_14

Vancouver

Bogdanov A, Shchegoleva N, Khvatov V, Kiyamov J, Dik G, Rakhmatullayev I et al. Integration of PBFT and Raft Algorithms with Recurrent Neural Networks to Improve the Reliability of Distributed Systems. In Gervasi O, Murgante B, Garau C, Taniar D, C. Rocha AMA, Faginas Lago MN, editors, Computational Science and Its Applications -- ICCSA 2024 Workshops. Cham: Springer Nature. 2024. p. 226-237. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-031-65154-0_14

Author

Bogdanov, Alexander ; Shchegoleva, Nadezhda ; Khvatov, Valery ; Kiyamov, Jasur ; Dik, Gennady ; Rakhmatullayev, Ilkhom ; Ergashev, Shakhboz ; Umurzakov, Oybek. / Integration of PBFT and Raft Algorithms with Recurrent Neural Networks to Improve the Reliability of Distributed Systems. Computational Science and Its Applications -- ICCSA 2024 Workshops. editor / Osvaldo Gervasi ; Beniamino Murgante ; Chiara Garau ; David Taniar ; Ana Maria A. C. Rocha ; Maria Noelia Faginas Lago. Cham : Springer Nature, 2024. pp. 226-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{7cb4c1e3615248e19f538aaa3e6190da,
title = "Integration of PBFT and Raft Algorithms with Recurrent Neural Networks to Improve the Reliability of Distributed Systems",
abstract = "The combined approach proposes the use of PBFT and Raft to ensure data consistency and fault tolerance in the system, and also integrates recurrent neural networks to analyze and predict the behavior of nodes in the network. RNNs can be used to detect anomalies, predict system load, and analyze time series data related to node operation. The proposed combined approach opens up new prospects for the development of distributed systems, increasing their reliability, fault tolerance and adaptability to changing conditions. Further research in this direction could lead to more efficient and secure distributed systems that can efficiently handle complex real-world scenarios.",
keywords = "Distributed Systems, PBFT, Raft, Recurrent Neural Networks, Reliability Ensuring",
author = "Alexander Bogdanov and Nadezhda Shchegoleva and Valery Khvatov and Jasur Kiyamov and Gennady Dik and Ilkhom Rakhmatullayev and Shakhboz Ergashev and Oybek Umurzakov",
year = "2024",
month = jul,
day = "30",
doi = "10.1007/978-3-031-65154-0_14",
language = "English",
isbn = "978-3-031-65154-0",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "226--237",
editor = "Osvaldo Gervasi and Beniamino Murgante and Chiara Garau and David Taniar and {C. Rocha}, {Ana Maria A.} and {Faginas Lago}, {Maria Noelia}",
booktitle = "Computational Science and Its Applications -- ICCSA 2024 Workshops",
address = "Germany",
note = "The 24th International Conference on Computational Science and Its Applications, ICCSA 2024, ICCSA ; Conference date: 01-07-2024 Through 04-07-2024",
url = "https://2024.iccsa.org/",

}

RIS

TY - GEN

T1 - Integration of PBFT and Raft Algorithms with Recurrent Neural Networks to Improve the Reliability of Distributed Systems

AU - Bogdanov, Alexander

AU - Shchegoleva, Nadezhda

AU - Khvatov, Valery

AU - Kiyamov, Jasur

AU - Dik, Gennady

AU - Rakhmatullayev, Ilkhom

AU - Ergashev, Shakhboz

AU - Umurzakov, Oybek

PY - 2024/7/30

Y1 - 2024/7/30

N2 - The combined approach proposes the use of PBFT and Raft to ensure data consistency and fault tolerance in the system, and also integrates recurrent neural networks to analyze and predict the behavior of nodes in the network. RNNs can be used to detect anomalies, predict system load, and analyze time series data related to node operation. The proposed combined approach opens up new prospects for the development of distributed systems, increasing their reliability, fault tolerance and adaptability to changing conditions. Further research in this direction could lead to more efficient and secure distributed systems that can efficiently handle complex real-world scenarios.

AB - The combined approach proposes the use of PBFT and Raft to ensure data consistency and fault tolerance in the system, and also integrates recurrent neural networks to analyze and predict the behavior of nodes in the network. RNNs can be used to detect anomalies, predict system load, and analyze time series data related to node operation. The proposed combined approach opens up new prospects for the development of distributed systems, increasing their reliability, fault tolerance and adaptability to changing conditions. Further research in this direction could lead to more efficient and secure distributed systems that can efficiently handle complex real-world scenarios.

KW - Distributed Systems

KW - PBFT

KW - Raft

KW - Recurrent Neural Networks

KW - Reliability Ensuring

UR - https://www.mendeley.com/catalogue/954466b9-4d1b-3227-8dec-2944294b2620/

U2 - 10.1007/978-3-031-65154-0_14

DO - 10.1007/978-3-031-65154-0_14

M3 - Conference contribution

SN - 978-3-031-65154-0

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 226

EP - 237

BT - Computational Science and Its Applications -- ICCSA 2024 Workshops

A2 - Gervasi, Osvaldo

A2 - Murgante, Beniamino

A2 - Garau, Chiara

A2 - Taniar, David

A2 - C. Rocha, Ana Maria A.

A2 - Faginas Lago, Maria Noelia

PB - Springer Nature

CY - Cham

T2 - The 24th International Conference on Computational Science and Its Applications, ICCSA 2024

Y2 - 1 July 2024 through 4 July 2024

ER -

ID: 122619364