Published July 31, 2019 | Version v1
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Architecting Data Consistency in Distributed Cloud Systems

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Ensuring data consistency across geographically distributed cloud systems is a fundamental challenge in modern computing. As enterprises adopt multi region, multi cloud, and edge integrated deployments, architects must balance the competing demands of strong consistency, low latency, and high availability. This paper presents a comprehensive architectural framework for managing and optimizing data consistency in large scale distributed environments. This paper examines the theoretical foundations of consistency through the CAP and PACELC models and evaluate practical mechanisms including consensus algorithms, quorum replication, and conflict-free data types. Building upon these principles, this paper proposes a modular, policy driven architecture that enables adaptive consistency guarantees based on workload characteristics, network conditions, and service level objectives. Experimental evaluations using synthetic and real-world workloads demonstrate that my approach achieves significant improvements in latency consistency trade-offs while maintaining operational resilience. The framework further extends to hybrid and edge cloud configurations, providing dynamic consistency adjustment to minimize propagation delay and staleness. By integrating theoretical rigor with empirical validation, this study bridges the gap between distributed systems research and practical cloud engineering. The results offer actionable insights for designing scalable, fault tolerant, and consistency aware data architectures suitable for next generation distributed cloud infrastructures.

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