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Geo-replication is essential in reliable large-scale cloud applications. We argue that existing replication solutions are too rigid to support today’s diversity of data consistency and performance requirements. Stabilizer is a flexible geo-replication library, supporting user-defined consistency models. The library achieves high performance using control-plane / data-plane separation: control events do not disrupt data flow. Our API offers simple control-plane operators that allow an application to define its desired consistency model: a stability frontier predicate. We build a wide-area K/V store with Stabilizer, a Dropbox-like application, and a prototype pub/sub system to show its versatility and evaluate its performance. When compared with a Paxos-based consistency protocol in an emulated Amazon EC2 wide-area network, experiments show that for a scenario requiring a more accurate consistency model, Stabilizer achieves a 24.75% latency performance improvement. Compared to Apache Pulsar in a real WAN environment, Stabilizer’s dynamic reconfiguration mechanism improves the pub/sub system performance significantly according to our experiment results.
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Li et al. (2022) studied this question.