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Cited by 638

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.

Zyzzyva: speculative byzantine fault tolerance

Kotla, Ramakrishna, Alvisi, Lorenzo, Dahlin, Mike, Clement, Allen, Wong, Edmund·2007·Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
DOI 10.1145/1294261.1294267
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Abstract

We present Zyzzyva, a protocol that uses speculation to reduce the cost and simplify the design of Byzantine fault tolerant state machine replication. In Zyzzyva, replicas respond to a client's request without first running an expensive three-phase commit protocol to reach agreement on the order in which the request must be processed. Instead, they optimistically adopt the order proposed by the primary and respond immediately to the client. Replicas can thus become temporarily inconsistent with one another, but clients detect inconsistencies, help correct replicas converge on a single total ordering of requests, and only rely on responses that are consistent with this total order. This approach allows Zyzzyva to reduce replication overheads to near their theoretical minimal.

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Metadata

Authors
Kotla, R., Alvisi, L., Dahlin, M., Clement, A., & Wong, E.
Venue
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Publisher
ACM
Published
2007-10-14
Pages
45-58
DOI
10.1145/1294261.1294267
OpenAlex
W2139359217
Cited by
638
References
50
FWCI
24.83
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Zyzzyva: speculative byzantine fault tolerance. https://app.cochat.ai/papers/10.1145/1294261.1294267
The paper
Kotla, R., Alvisi, L., Dahlin, M., Clement, A., & Wong, E. (2007). Zyzzyva: speculative byzantine fault tolerance. Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles. https://doi.org/10.1145/1294261.1294267

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.