Causal Consistency for Geo-Replicated Cloud Storage under Partial Replication

Min Shen, A. Kshemkalyani, T. Hsu
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引用次数: 29

Abstract

Data replication is a common technique used for fault-tolerance in reliable distributed systems. In geo-replicated systems and the cloud, it additionally provides low latency. Recently, causal consistency in such systems has received much attention. However, all existing works assume the data is fully replicated. This greatly simplifies the design of the algorithms to implement causal consistency. In this paper, we propose that it can be advantageous to have partial replication of data, and we propose two algorithms for achieving causal consistency in such systems where the data is only partially replicated. This is the first work that explores causal consistency for partially replicated geo-replicated systems. We also give a special case algorithm for causal consistency in the full-replication case.
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部分复制下两地复制云存储的因果一致性
数据复制是可靠分布式系统中用于容错的常用技术。在地理复制系统和云中,它还提供了低延迟。最近,这类系统的因果一致性受到了广泛关注。然而,所有现有的工作都假定数据是完全复制的。这大大简化了实现因果一致性的算法设计。在本文中,我们提出数据的部分复制可能是有利的,并且我们提出了两种算法,用于在数据仅部分复制的系统中实现因果一致性。这是第一个探索部分复制的地理复制系统的因果一致性的工作。我们还给出了全复制情况下因果一致性的一个特例算法。
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