Abstract Scalability and availability in a large-scale distributed database is determined by the consistency strategies used by the transactions. Most of the big data applications demand consistency and availability at the same time. However, a suitable transaction model that handles the trade-obetween availability and consistency is presently lacking. In this article, we have proposed a hierarchical transaction model that supports multiple consistency levels for data items in a large-scale replicated database. The data items have been classified into different categories based on their consistency requirement, computed using a data mining algorithm. Thereafter, these have been mapped to the appropriate consistency level in the hierarchy. This allows parallel execution of several transactions belonging to each level. The topmost level called the Serializable (SR) level follows strong consistency applicable to data items that are mostly read and updated both. The next level of consistency, Snapshot Isolation (SI), maps to data items which are mostly read and demand unblocking read. Data items which are mostly updated do not follow strict consistent snapshot and have been mapped to the next lower level called Non- monotonic Snapshot Isolation (NMSI). The lowest level in the hierarchy correspond to data items for which ordering of operations does not matter. This level is called the Asynchronous (ASYNC) level. We have tested the proposed transaction model with two different workloads on a test-bed designed following the TPC-C benchmark schema. The performance of the proposed model has been evaluated against other transaction models that support single consistency policy. The proposed model has shown promising results in terms of transaction throughput, commit rate and average latency.

中文翻译：

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基于数据挖掘的大规模复制数据库多级一致性管理分层事务模型

摘要 大型分布式数据库的可扩展性和可用性由事务使用的一致性策略决定。大多数大数据应用程序同时要求一致性和可用性。然而，目前缺乏处理可用性和一致性之间权衡的合适的事务模型。在本文中，我们提出了一种分层事务模型，该模型支持大规模复制数据库中数据项的多个一致性级别。数据项已根据其一致性要求分为不同的类别，使用数据挖掘算法计算。此后，这些已映射到层次结构中的适当一致性级别。这允许并行执行属于每个级别的多个事务。称为 Serializable (SR) 级别的最高级别遵循适用于主要读取和更新的数据项的强一致性。下一个一致性级别，即快照隔离 (SI)，映射到主要读取并需要解锁读取的数据项。大多数更新的数据项不遵循严格一致的快照，并且已映射到称为非单调快照隔离 (NMSI) 的下一个较低级别。层次结构中的最低级别对应于操作顺序无关紧要的数据项。此级别称为异步 (ASYNC) 级别。我们已经在遵循 TPC-C 基准模式设计的测试平台上使用两种不同的工作负载测试了提议的事务模型。已针对支持单一一致性策略的其他事务模型评估了所提出模型的性能。所提出的模型在事务吞吐量、提交率和平均延迟方面显示出有希望的结果。