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Consistency properties provided by most key-value stores can be classifiedinto sequential consistency and eventual consistency. The former is easier toprogram with but suffers from lower performance whereas the latter suffers frompotential anomalies while providing higher performance. We focus on the problemof what a designer should do if he/she has an algorithm that works correctlywith sequential consistency but is faced with an underlying key-value storethat provides a weaker consistency. We propose a detect-rollback basedapproach: The designer identifies a correctness predicate, say $P$, andcontinues to run the protocol, as our system monitors $P$. If $P$ is violated(because of weaker consistency), the system rolls back and resumes thecomputation at a state where $P$ holds.  We evaluate this approach with graph-based applications running on theVoldemort key-value store. Our experiments with deployment on Amazon AWS EC2instances shows that using eventual consistency with monitoring can provide a$50\%$ -- $80\%$ increase in throughput when compared with sequentialconsistency. We also observe that the overhead of the monitoring itself was low(typically less than $4\%$) and the latency of detecting violations was small.In particular, in a scenario designed to intentionally cause a large number ofviolations, more than $99.9\%$ of violations were detected in less than 50milliseconds in regional networks, and in less than 3 seconds in globalnetworks.  We find that for some applications, frequent rollback can cause the programusing eventual consistency to effectively \textit{stall}. We propose alternatemechanisms for dealing with re-occurring rollbacks. Overall, for applicationsconsidered in this paper, we find that even with rollback, eventual consistencyprovides better performance than using sequential consistency.

Using Weaker Consistency Models with Monitoring and Recovery for Improving Performance of Key-Value Stores