A communication-efficient canonical form for fault-tolerant distributed protocols
PODC '86 Proceedings of the fifth annual ACM symposium on Principles of distributed computing
Concurrency control and recovery in database systems
Concurrency control and recovery in database systems
Knowledge and common knowledge in a distributed environment
Journal of the ACM (JACM)
Knowledge and common knowledge in a byzantine environment: crash failures
Information and Computation
Common knowledge and consistent simultaneous coordination
Proceedings of the 4th international workshop on Distributed algorithms
The ESTEREL synchronous programming language: design, semantics, implementation
Science of Computer Programming
Reasoning about knowledge
ACM Transactions on Computer Systems (TOCS)
Synchronous Programming of Reactive Systems
Synchronous Programming of Reactive Systems
Virtual infrastructure for collision-prone wireless networks
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
OCD: obsessive consensus disorder (or repetitive consensus)
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Continuous consensus with ambiguous failures
ICDCN'08 Proceedings of the 9th international conference on Distributed computing and networking
Long live continuous consensus
DISC'07 Proceedings of the 21st international conference on Distributed Computing
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Continuous consensus is the problem of having each process i maintain at each time k an up-to-date core Mi[k] of information about the past, so that the cores are guaranteed to be identical. A simple algorithm for continuous consensus in fault-prone systems called CONCON is presented, based on a knowledge-based analysis. Continuous consensus is shown to be closely related to common knowledge. Via this connection, the characterization of common knowledge in systems with crash and omission failures by Moses and Tuttle is used to prove that CONCON is optimal---it produces the largest possible core at any given time. Finally, we modify the CONCON algorithm to obtain a uniform solution, in which all processes (faulty and nonfaulty) obtain the same core information at any given time.