| 基于一致性的分布式云存储系统扩展数据安全压缩延迟优化 |
投稿时间：2025-02-17 修订日期：2025-04-11  点此下载全文 |
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| 中文摘要:面向分布式云存储系统扩展数据进行安全压缩处理时，主要通过分散式加密算法保证数据安全，其操作环节复杂，导致压缩延迟较大。因此，提出基于一致性的分布式云存储系统扩展数据安全压缩延迟优化方法。依托于一致性思想对扩展数据进聚类处理，划分出多个独立的数据单元，再运用联合随机模型完成集中式高速加密处理。以门控循环神经网络为核心，构建数据压缩智能化模型。面向数据安全压缩参数，设置以最小化压缩耗时为目标的延迟优化数学模型。最后，应用遗传算法展开迭代优化求解，得到最佳数据压缩策略。实验结果表明：优化后的数据安全压缩方法实施后，产生的平均压缩耗时总是低于20ms，极大减少了数据压缩延迟。 |
| 中文关键词:分布式云存储系统 扩展数据 一致性 聚类处理 加密 数据压缩 |
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| Consistency based distributed cloud storage system extends data security compression delay optimization |
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| Abstract:When expanding data for distributed cloud storage systems for secure compression processing, decentralized encryption algorithms are mainly used to ensure data security. The complex operation process leads to significant compression latency. Therefore, a consistency based distributed cloud storage system extension data security compression delay optimization method is proposed. Based on the idea of consistency, the extended data is clustered and divided into multiple independent data units, and then a joint random model is used to complete centralized high-speed encryption processing. Build an intelligent data compression model with gate controlled recurrent neural network as the core. Set a delay optimization mathematical model with the goal of minimizing compression time for data security compression parameters. Finally, the genetic algorithm is applied for iterative optimization to obtain the optimal data compression strategy. The experimental results show that after implementing the optimized data security compression method, the average compression time generated is always less than 20ms, greatly reducing data compression latency. |
| keywords:Distributed cloud storage system Expand data Uniformity Clustering processing Encryption Data compression |
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