Technologies such as the Internet of Things are driving exponential growth in data volume, while traditional data management systems are difficult to solve the challenges of "data chimney" and cross domain sharing due to inconsistent data standards. Data oriented architecture (DOA) focuses on data as its core and builds a data ecosystem through a data registry center (DRC). However, distributed DRC faces high availability and efficiency bottlenecks in high concurrency scenarios - traditional Raft protocol dual center solutions face issues such as storage and write congestion, and "leaderless" fault selection. High availability cluster solutions lack fault tolerance and recovery capabilities. This study proposes a distributed DRC high availability scheme based on role partitioning, which includes: dividing NameNode nodes into four roles: Leader, Inheritor, etc., clarifying read-write division of labor and fault takeover mechanism; Optimize Raft protocol to avoid "leaderless state" and shorten election time; Introducing vector clock to achieve data version conflict detection and improve synchronization efficiency. The experiment shows that the performance of the improved scheme is significantly improved: the write response delay is stable at 0.16-0.75ms (the highest in the original scheme was 4.74ms), the fault recovery time is reduced to 500-800ms (the original 1400-1700ms), and the synchronization time of the three/five machine mode is 428.4ms and 588.2ms, respectively. This solution solves the problem of multi machine data consistency through algorithm innovation, providing high-performance technical support for data sharing under DOA architecture.

Yizhou Meng. Exploration of State Machine-Driven Access Control Mechanisms in Distributed Systems. International Journal of Neural Network (2025), Vol. 4, Issue 1: 23-31. https://doi.org/10.38007/NN.2025.040103.

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