This paper examines possibilities for improving the existing strategies of consistency management for highly-distributed transactional database in a hybrid cloud environment. With a detailed analysis of the existing consistency models for distributed database and standard strategies including Classic, Quorum and Tree Based Consistency (TBC), it is concluded that an improved advanced model of so-called visible adaptive consistency needs to be applied in a highly-distributed cloud environment, as necessary and sufficient degree of synchronization of all replicas. Along with the proposed model, research and development of an advanced novel strategy for consistency management Rose TBC (R-TBC) approach has been conducted, by improving standard TBC approach. Regarding implementation, a specific agglomerative Rose Tree Algorithm (RTA) has been developed, based on Bayesian hierarchical clustering and Graph Partitioning Algorithm - Multidimensional Data Clustering (GPA-MDC) intelligent partitioning of transactional Cloud Database Management System (CDBMS). The final result is constructed R-TBC model that changes in accordance with dynamic changes of entire heterogeneous CDBMS environment.

中文翻译：

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使用新颖的R-TBC / RTA方法在混合云环境中对高度分布式事务数据库进行高级一致性管理

本文探讨了在混合云环境中改进现有的用于高度分布式事务数据库的一致性管理策略的可能性。详细分析了分布式数据库的现有一致性模型以及包括经典，基于仲裁和基于树的一致性（TBC）在内的标准策略，可以得出结论，需要在高度集成的应用程序中使用一种改进的高级模型，即所谓的可见自适应一致性。分布式云环境中，根据需要和所有副本的足够程度的同步。除提出的模型外，还通过改进标准的TBC方法，研究和开发了一种用于一致性管理的高级新策略Rose Rose TBC（R-TBC）方法。关于实施，已经开发了一种特定的聚集式玫瑰树算法（RTA），基于贝叶斯层次聚类和图分区算法-多维数据聚类（GPA-MDC）的事务性云数据库管理系统（CDBMS）智能分区。最终结果是构建了R-TBC模型，该模型根据整个异构CDBMS环境的动态变化而变化。