POSSIBILITIES OF USING AGENT-BASED MODELING SOFTWARE TOOLS FOR REPLICATION PROCESSES IN DISTRIBUTED SYSTEMS

Authors

Keywords:

Distributed Systems, Agent-Based Modeling, Python, Mesa

Abstract

Abstract. This paper explores the process of data replication in distributed computer systems using agent-based modeling. Replication ensures fault tolerance, reduces access latency, and improves system reliability. However, its implementation faces several challenges, including data consistency, load balancing, and protection against failures. Agent-based modeling allows for effective investigation of these aspects by simulating autonomous node agents. The study presents a replication model developed with the Mesa framework, featuring simulations across different network topologies and replication strategies. The dynamics of data distribution, failure rates, and query success rates were analyzed. The results confirm the relevance of the agent-based approach for studying replication processes in complex network environments.

Author Biographies

Nataliya Poluektova, Zaporizhzhya Institute of Economics and Information Technologies

Professor of IT Dept.

Dr.Econ.Sci, Associated professor

Gennadiy Turovtsev, Zaporizhzhya Institute of Economics and Information Technologies

Rector

Dr. Phys.-Math. Sci., Professor

Anatoliy Pereverzev, Zaporizhzhya Institute of Economics and Information Technologies

Vice-Rector of Scientific Work

Dr.Tech.Sci., Professor

References

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Mesa, “Mesa: Agent-based modeling in Python,” [Online]. Available: https://mesa.readthedocs.io/latest/getting_started.html

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Published

2023-12-21

How to Cite

Poluektova, N., Turovtsev, G., & Pereverzev, A. (2023). POSSIBILITIES OF USING AGENT-BASED MODELING SOFTWARE TOOLS FOR REPLICATION PROCESSES IN DISTRIBUTED SYSTEMS. igital conomy and nformation echnologies, 2(1). etrieved from http://journals.zieit.edu.ua/index.php/deit/article/view/22

Issue

Vol. 2 No. 1 (2023): Digital Economy and Information Technologies

Section

Computer engineering

License

Copyright (c) 2023 N.Poiluektova, G.Turovtsev, A.Pereverzev

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.