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In this paper, we focus on the implementation of distributed programs inusing a key-value store where the state of the nodes is stored in a replicatedand partitioned data store to improve performance and reliability. Applicationsof such algorithms occur in weather monitoring, social media, etc. We arguethat these applications should be designed to be stabilizing so that theyrecover from an arbitrary state to a legitimate state. Specifically, if we usea stabilizing algorithm then we can work with more efficient implementationsthat provide eventual consistency rather than sequential consistency where thedata store behaves as if there is just one copy of the data. We find that,although the use of eventual consistency results in consistency violationfaults (cvf) where some node executes its action incorrectly because it relieson an older version of the data, the overall performance of the resultingprotocol is better. We use experimental analysis to evaluate the expectedimprovement. We also identify other variations of stabilization that canprovide additional guarantees in the presence of eventual consistency.Finally,we note that if the underlying algorithm is not stabilizing, even a single \cvfmay cause the algorithm to fail completely thereby making it impossible tobenefit from this approach.

Benefit of Self-Stabilizing Protocols in Eventually Consistent Key-Value Stores: A Case Study