一种基于数据相关性的优化数据一致性维护方法

针对数据一致性中的数据相关性问题,提出一种优化的数据一致性维护方法.在该方法中,数据对象按固定大小分块,并以数据块作为数据管理的基本单位;数据更新利用Bloom filter技术压缩表示,并进行双路径传播;发起方和协商方在一致性维护过程中,分别调用各自的协商算法检测和解决更新冲突;动态数据管理算法调节数据更新过程中的动态数据块变化,对数据块进行合并或分解.模拟测试结果表明,在选取适当范围内的分块大小时,该方法在一致性维护开销、动态性和鲁棒性方面均具有较好的性能.文中给出了选定适当分块大小的指导性方法.

An Optimistic Data Consistency Maintenance Method Based on Data Dependence

This paper proposes an optimistic data consistency method according to the question about data dependence in data consistency.In the method,data object is partitioned into data blocks by fixed size as the basic unit of data management.Updates are compressed by Bloom filter technique and propagated in double-path.Negotiation algorithms detect and reconcile update conflicts,and dynamic data management algorithms accommodate dynamic data processing.The results of the performance evaluation show that it is an efficient method to achieve consistency,good dynamic property,and strong robustness when choosing the size of data block appropriately.At the same time,a feasible way is put forward on how to choose appropriate data block size.