preprint · arXiv (Cornell University) · 2019
Replication-Aware Linearizability
Constantin Enea, Süha Orhun Mutluergil, Gustavo Petri, Chao Wang
Cited by 3Open access
Abstract
Geo-distributed systems often replicate data at multiple locations to achieve availability and performance despite network partitions. These systems must accept updates at any replica and propagate these updates asynchronously to every other replica. Conflict-Free Replicated Data Types (CRDTs) provide a principled approach to the problem of ensuring that replicas are eventually consistent despite the asynchronous delivery of updates. We address the problem of specifying and verifying CRDTs, introducing a new correctness criterion called Replication-Aware Linearizability. This criterion is inspired by linearizability, the de-facto correctness criterion for (shared-memory) concurrent data structures. We argue that this criterion is both simple to understand, and it fits most known implementations of CRDTs. We provide a proof methodology to show that a CRDT satisfies replication-aware linearizability which we apply on a wide range of implementations. Finally, we show that our criterion can be leveraged to reason modularly about the composition of CRDTs.
Cite this paper
Enea, C., Mutluergil, S. O., Petri, G., & Wang, C. (2019). Replication-Aware Linearizability. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1903.06560
Enea, Constantin, et al. “Replication-Aware Linearizability.” arXiv (Cornell University), 2019, https://doi.org/10.48550/arxiv.1903.06560.
Enea, Constantin, Süha Orhun Mutluergil, Gustavo Petri, and Chao Wang. 2019. “Replication-Aware Linearizability.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1903.06560.
Enea, C. et al. (2019) ‘Replication-Aware Linearizability’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1903.06560.
@misc{enea2019replication,
title = {{Replication-Aware Linearizability}},
author = {Enea, Constantin and Mutluergil, Süha Orhun and Petri, Gustavo and Wang, Chao},
journal = {arXiv (Cornell University)},
year = {2019},
doi = {10.48550/arxiv.1903.06560}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
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