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Future Internet
Volume 17
Issue 10
10.3390/fi17100442
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Opportunities for Adapting Data Write Latency in Geo-Distributed Replicas of Multicloud Systems

by
Olha Kozina
1,*,
José Machado
2,*,
Maksym Volk
3,
Hennadii Heiko
4,
Volodymyr Panchenko
4,
Mykyta Kozin
3 and
Maryna Ivanova
5
1
AFOREHAND Studio, 61072 Kharkіv, Ukraine
2
MEtRICs Research Centre, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
3
Department of Electronic Computers, Kharkiv National University of Radio Electronics, 61166 Kharkіv, Ukraine
4
Computer Engineering and Programming Department, National Technical University Kharkiv Polytechnic Institute, 61002 Kharkіv, Ukraine
5
Department of Mechanical Engineering Technology and Metal-Cutting Machines, National Technical University Kharkiv Polytechnic Institute, 61002 Kharkіv, Ukraine
*
Authors to whom correspondence should be addressed.
Future Internet 2025, 17(10), 442; https://doi.org/10.3390/fi17100442 (registering DOI)
Submission received: 20 August 2025 / Revised: 17 September 2025 / Accepted: 22 September 2025 / Published: 28 September 2025
(This article belongs to the Special Issue Artificial Intelligence and Control Systems for Industry 4.0 and 5.0)
Versions Notes

Abstract

This paper proposes an AI-based approach to adapting the data write latency in multicloud systems (MCSs) that supports data consistency across geo-distributed replicas of cloud service providers (CSPs). The proposed approach allows for dynamically forming adaptation scenarios based on the proposed model of multi-criteria optimization of data write latency. The generated adaptation scenarios are aimed at maintaining the required data write latency under changes in the intensity of the incoming request flow and network transmission time between replicas in CSPs. To generate adaptation scenarios, the features of the algorithmic Latord method of data consistency, are used. To determine the threshold values and predict the external parameters affecting the data write latency, we propose using learning AI models. An artificial neural network is used to form rules for changing the parameters of the Latord method when the external operating conditions of MCSs change. The features of the Latord method that influence data write latency are demonstrated by the results of simulation experiments on three MCSs with different configurations. To confirm the effectiveness of the developed approach, an adaptation scenario was considered that allows reducing the data write latency by 13% when changing the standard deviation of network transmission time between DCs of MCS.
Keywords: multicloud systems; data writing latency; optimization; data consistency method multicloud systems; data writing latency; optimization; data consistency method

Share and Cite

MDPI and ACS Style

Kozina, O.; Machado, J.; Volk, M.; Heiko, H.; Panchenko, V.; Kozin, M.; Ivanova, M. Opportunities for Adapting Data Write Latency in Geo-Distributed Replicas of Multicloud Systems. Future Internet 2025, 17, 442. https://doi.org/10.3390/fi17100442

AMA Style

Kozina O, Machado J, Volk M, Heiko H, Panchenko V, Kozin M, Ivanova M. Opportunities for Adapting Data Write Latency in Geo-Distributed Replicas of Multicloud Systems. Future Internet. 2025; 17(10):442. https://doi.org/10.3390/fi17100442

Chicago/Turabian Style

Kozina, Olha, José Machado, Maksym Volk, Hennadii Heiko, Volodymyr Panchenko, Mykyta Kozin, and Maryna Ivanova. 2025. "Opportunities for Adapting Data Write Latency in Geo-Distributed Replicas of Multicloud Systems" Future Internet 17, no. 10: 442. https://doi.org/10.3390/fi17100442

APA Style

Kozina, O., Machado, J., Volk, M., Heiko, H., Panchenko, V., Kozin, M., & Ivanova, M. (2025). Opportunities for Adapting Data Write Latency in Geo-Distributed Replicas of Multicloud Systems. Future Internet, 17(10), 442. https://doi.org/10.3390/fi17100442

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