Consistent Recovery Technology of Cross-Domain Data Storage Under the Distributed Edge Cloud
Zhengxiong Mao, Feng Yang, Chenglin Li, Yuxu Chen · International Journal of High Speed Electronics and Systems · 2025
With the development of the Internet of Things and 5G technology, distributed edge cloud cross-domain storage has become key, but the problem of data consistency restricts its application. This study combines theoretical analysis and experimental verification to deeply analyze the root causes of data inconsistency and the limitations of existing recovery methods. It proposes a novel data consistency recovery technology based on innovative algorithms and strategies, which includes data status monitoring, differential data positioning, and a consistency recovery module. The technology enables quick and accurate recovery of data. Experiments conducted in a simulated distributed edge cloud environment show that the proposed technique significantly outperforms traditional log-based and vote-based recovery methods. Specifically, the average recovery time was reduced to 15[Formula: see text]s, compared to 30[Formula: see text]s for the log-based method and 25[Formula: see text]s for the vote-based method. The recovery success rate achieved 95%, much higher than the 80% and 85% success rates of the log-based and vote-based methods, respectively. Additionally, the data accuracy of the recovery process was over 99.9%, a significant improvement over the traditional methods. These results highlight the effectiveness of the proposed approach in enhancing recovery time, success rate, and data accuracy. In future work, integrating blockchain and artificial intelligence technologies could further enhance applications in complex network environments and large-scale data scenarios, leading to customized industry-specific solutions.