Enabling Low-Latency Distributed Updates with Metadata-Guided Synchronization and Seamless Service Transition

Le résumé fourni par la source

In modern high-performance distributed systems, ensuring real-time data consistency and seamless service continuity under massive concurrent updates presents significant engineering challenges. This paper introduces a scalable and efficient architecture for distributed data management, enabling fine-grained incremental updates, low-overhead synchronization, and consistent service transitions. The approach leverages metadata-driven version tracking to isolate update deltas and reduce storage and network overhead. A feedback-regulated multicast protocol ensures efficient data dissemination across nodes without compromising consistency. Additionally, the system employs a layered execution model that allows services to switch versions at runtime with minimal impact on availability. Experiments conducted in a production-simulated environment demonstrate that the proposed architecture improves compression throughput by over 30% and keeps incremental backup size under 5% of the full dataset. The framework can boost multicast synchronization performance by more than 16× over TCP, and reduce update latency under high concurrency by 44%. The framework is applicable to latency-critical, data-intensive domains such as workflow execution, policy-driven systems, and transaction-heavy platforms.

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