This work presents Speculative Transaction Replication (STR), a protocol that exploits transparent speculation techniques to enhance performance of geo-distributed, partially replicated transactional data stores. In addition, we define a new consistency model, Speculative Snapshot Isolation (SPSI), a variant of Snapshot Isolation (SI), which shelters applications from the subtle anomalies that arise when using speculative transaction processing techniques.

STR provides a form of speculation that is fully transparent for programmers (it does not expose the effects of misspeculations to clients). Since the speculation techniques employed by STR satisfy SPSI, they can be leveraged by application programs in a transparent way, without requiring any source-code modification to applications designed to operate using SI. STR combines two key techniques: speculative reads, which allow transactions to observe pre-committed versions, which can reduce the ‘effective duration’ of pre-commit locks and enhance throughput; Precise Clocks, a novel timestamping mechanism that uses per-item timestamps with physical clocks, which together greatly enhance the probability of successful speculation.

We assess STR’s performance on up to nine geo-distributed Amazon EC2 data centers, using both synthetic benchmarks as well as realistic benchmarks (TPC-C and RUBiS). Our evaluation shows that STR achieves throughput gains up to 11$\times$ and latency reduction up to 10$\times$ (with respect to non-speculative systems that ensure SI) in workloads characterized by low inter-data center contention. Furthermore, thanks to a self-tuning mechanism that dynamically and transparently enables and disables speculation, STR offers robust performance even when faced with unfavorable workloads that suffer from high misspeculation rates.

这项工作介绍了推测性事务复制（STR），该协议利用透明的推测技术来增强地理分布的，部分复制的事务性数据存储的性能。此外，我们定义了一个新的一致性模型，即推测快照隔离（SPSI），它是快照隔离（SI）的一种变体，它使应用程序免受使用推测事务处理技术时出现的细微异常的影响。

STR提供了一种对程序员完全透明的推测形式（它不会向客户端暴露错误推测的影响）。由于STR采用的推测技术满足SPSI，因此应用程序可以透明方式利用它们，而无需对设计为使用SI进行操作的应用程序进行任何源代码修改。STR结合了两种关键技术：投机读取，它使事务能够观察预提交的版本，这可以减少预提交锁的“有效持续时间”并提高吞吐量。精确时钟，一种新颖的时间戳机制，将每个项目的时间戳与物理时钟结合使用，这大大提高了成功进行推测的可能性。

我们使用综合基准以及实际基准（TPC-C和RUBiS）评估STR在多达9个地理分布的Amazon EC2数据中心上的性能。我们的评估表明，STR可实现高达11的吞吐量增长$\times$ 和延迟减少多达10$\times$（关于确保SI的非推测性系统）在以低数据中心间争用为特征的工作负载中。此外，得益于自动调整机制，该机制可以动态，透明地启用和禁用推测，即使STR面临因错误推测率高而带来的不利工作负载时，STR也可以提供强大的性能。