When Scalability Meets Consistency: Genuine Multiversion Update-Serializable Partial Data Replication
When Scalability Meets Consistency: Genuine Multiversion Update-Serializable Partial Data Replication
复制标题
当可扩展性满足一致性：真正的多版本更新-可序列化部分数据复制
DOI：
10.1109/icdcs.2012.55
复制
发表时间：
2012
期刊：
影响因子：
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通讯作者：
L. Rodrigues
中科院分区：
文献类型：
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作者：
Sebastiano Peluso;P. Ruivo;P. Romano;F. Quaglia;L. Rodrigues
在这篇文章中，我们介绍GMU，一个真正的部分复制协议的事务系统，它利用了创新的，高度可扩展的，分布式的多版本方案。与现有的基于多版本的解决方案不同，GMU不依赖于全局逻辑时钟，全局逻辑时钟代表争用点，可能会限制系统的可扩展性。此外，GMU从不中止只读事务，并将它们从分布式验证方案中保留下来。这使得GMU在存在读取密集型工作负载的情况下特别高效，这是广泛的实际应用程序的典型情况。GMU保证扩展更新可串行化性（EUS）隔离级别。这种一致性标准特别有吸引力，因为它足够强，即使对于非常苛刻的应用程序（如TPC-C）也能确保正确性，但也足够弱，允许高效和可扩展的实现，如GMU。此外，与文献中提出的几种宽松的一致性模型不同，EUS具有简单直观的语义，因此对于普通程序员来说是一个有吸引力的、可扩展的一致性模型。我们将GMU协议集成到一个流行的开源内存事务数据网格中，即Infinispan。在异构实验平台上进行的大规模实验研究的基础上，并使用行业标准的基准测试（即TPC-C和YCSB），我们表明，GMU实现线性可扩展性，它引入了可以忽略不计的开销（小于10%），相对于解决方案，确保非序列化语义，在广泛的工作负载。
In this article we introduce GMU, a genuine partial replication protocol for transactional systems, which exploits an innovative, highly scalable, distributed multiversioning scheme. Unlike existing multiversion-based solutions, GMU does not rely on a global logical clock, which represents a contention point and can limit system scalability. Also, GMU never aborts read-only transactions and spares them from distributed validation schemes. This makes GMU particularly efficient in presence of read-intensive workloads, as typical of a wide range of real-world applications. GMU guarantees the Extended Update Serializability (EUS) isolation level. This consistency criterion is particularly attractive as it is sufficiently strong to ensure correctness even for very demanding applications (such as TPC-C), but is also weak enough to allow efficient and scalable implementations, such as GMU. Further, unlike several relaxed consistency models proposed in literature, EUS has simple and intuitive semantics, thus being an attractive, scalable consistency model for ordinary programmers. We integrated the GMU protocol in a popular open source in-memory transactional data grid, namely Infinispan. On the basis of a large scale experimental study performed on heterogeneous experimental platforms and using industry standard benchmarks (namely TPC-C and YCSB), we show that GMU achieves linear scalability and that it introduces negligible overheads (less than 10%), with respect to solutions ensuring non-serializable semantics, in a wide range of workloads.