UniStore：因果和强一致性的容错婚姻（扩展版）
UniStore: A fault-tolerant marriage of causal and strong consistency (extended version)
摘要：
现代在线服务依赖于跨地理分布的数据中心复制其数据的数据存储。在此类数据存储中提供强一致性会导致高延迟，并使系统容易受到网络分区的影响。放松一致性的替代方案违反了关键的正确性属性。一种折衷办法是允许多个一致性级别在数据存储中共存。在本文中，我们提出了UniStore，第一个容错和可扩展的数据存储，结合因果关系和强一致性。我们在UniStore中解决的关键挑战是在数据中心故障的情况下保持活跃性：如果强事务依赖于后来因故障而丢失的因果事务，则可能会受到影响。UniStore确保这种情况不会发生，同时仅在必要时为因果交易支付耐久性成本。我们使用微基准测试和示例应用程序评估Amazon EC2上的UniStore。我们的研究结果表明，UniStore有效地和可扩展地结合了因果关系和强一致性。
英文摘要：
Modern online services rely on data stores that replicate their data across geographically distributed data centers. Providing strong consistency in such data stores results in high latencies and makes the system vulnerable to network partitions. The alternative of relaxing consistency violates crucial correctness properties. A compromise is to allow multiple consistency levels to coexist in the data store. In this paper we present UniStore, the first fault-tolerant and scalable data store that combines causal and strong consistency. The key challenge we address in UniStore is to maintain liveness despite data center failures: this could be compromised if a strong transaction takes a dependency on a causal transaction that is later lost because of a failure. UniStore ensures that such situations do not arise while paying the cost of durability for causal transactions only when necessary. We evaluate UniStore on Amazon EC2 using both microbenchmarks and a sample application. Our results show that UniStore effectively and scalably combines causal and strong consistency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
