Abstract

Geo-replicated distributed systems have become the backbone of modern cloud infrastructure, powering applications ranging from global e-commerce platforms to real-time collaboration tools. However, replicating data across geographically dispersed data centers introduces a fundamental tension between consistency, availability, and latency, as captured by the CAP and PACELC theorems. This paper investigates how different consistency models such as strong, eventual, causal, and session consistency perform under real-world workloads, and how modern conflict resolution techniques handle concurrent updates. We designed an experimental testbed spanning three simulated regions and evaluated four popular replication strategies using YCSB-style workloads. Our results show that causal consistency offers a practical middle ground, delivering nearly 68% lower write latency than strong consistency while keeping the stale read rate below 4%. We also observed that CRDT-based conflict resolution reduces merge failures by roughly 41% compared to traditional last-writer-wins approaches, though at the cost of slightly higher memory overhead. The paper further discusses trade-offs, deployment considerations, and open research problems including hybrid consistency, machine-learning-driven conflict prediction, and consistency-aware scheduling. Findings are meant to help system architects choose consistency guarantees that fit their application semantics rather than defaulting to one extreme of the spectrum. Overall, the study contributes empirical evidence, a comparative framework, and practical recommendations that connect classical distributed systems theory with the pragmatic needs of today's globally distributed workloads.

Citation details of the article

Journal: International Journal of Applied Mathematics
Journal ISSN (Print): ISSN 1311-1728
Journal ISSN (Electronic): ISSN 1314-8060
Volume: 35
Issue: 3
Year: 2022

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