[2407.01720] Linearizability and State-Machine Replication: Is it a match?
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Computer Science > Distributed, Parallel, and Cluster Computing
arXiv:2407.01720 (cs)
[Submitted on 1 Jul 2024]
Title:Linearizability and State-Machine Replication: Is it a match?
Authors:Franz J. Hauck, Alexander Heß<br>View a PDF of the paper titled Linearizability and State-Machine Replication: Is it a match?, by Franz J. Hauck and Alexander He{\ss}
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Abstract:Linearizability is a well-known correctness property for concurrent and distributed systems. In the past, it was also used to prove the design and implementation of replicated state-machines correct. State-machine replication (SMR) is a concept to achieve fault-tolerant services by replicating the application code and maintaining its deterministic execution in all correct replicas. Correctness of SMR needs to address both, the execution of the application code -- the state machine -- and the replication framework that takes care of communication, checkpointing and recovery. We show that linearizability is overly restrictive for SMR as it excludes many applications from being replicated. It cannot deal with conditional waits and bidirectional data flows between concurrent executions. Further, linearizability does not allow for vertical composition, e.g., nested invocations. In this work we argue that interval linearizability, a recently defined relaxation of linearizability, is the appropriate correctness criterion for SMR systems. This no longer puts any constraints on the application code. Instead the focus for correctness proofs of an SMR system is on the deterministic execution of the application within correct replicas under the assumptions of the chosen failure model. Thus, this work not only clears some myths about linearizable SMR but also relieves correctness proofs from linearizability proofs.
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Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as:<br>arXiv:2407.01720 [cs.DC]
(or<br>arXiv:2407.01720v1 [cs.DC] for this version)
https://doi.org/10.48550/arXiv.2407.01720
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arXiv-issued DOI via DataCite
Submission history<br>From: Franz J. Hauck [view email]<br>[v1]<br>Mon, 1 Jul 2024 18:49:56 UTC (280 KB)
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