Evaluating Data Replication Models for Improving the Efficiency of Distributed Microservice Systems
DOI:
https://doi.org/10.47839/ijc.25.2.4659Keywords:
data replication, microservice architecture, distributed systems, performance, availability, data consistency, Docker, KubernetesAbstract
The object of the study is the process of data replication in distributed microservices systems. The subject of the study is the data replication models used in such systems. The aim of the study was to improve the efficiency of distributed microservices systems by optimizing the balance between performance, data consistency, and operational costs. Modern replication models – synchronous, asynchronous, quorum-based, CRDT, and blockchain – were analyzed in the context of their adaptation to microservices architectures. It was found that asynchronous models provide high performance under variable load but reduce data consistency; synchronous models ensure stable consistency at the cost of higher overhead; quorum-based models optimize the balance between availability and consistency, achieving 15–20% operational cost savings. A classification of replication models by performance, consistency, and fault tolerance was developed to facilitate the rapid selection of the optimal solution based on system priorities. Experimental studies were conducted using Docker and Kubernetes with Kafka, Redis, and CockroachDB under loads of up to 5000 requests per second. Comparative evaluation showed that CRDT models provide maximum performance, while quorum-based models offer reliability and cost efficiency. An approach for calculating operational costs was proposed, taking into account request intensity, network latency, and replica storage costs. The results enable informed selection of replication models and enhance the efficiency of microservices systems, particularly during migration from monolithic to containerized architectures and scaling of high-load telecommunications systems.
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