Complexity of network synchronization
Journal of the ACM (JACM)
Consensus in the presence of partial synchrony
Journal of the ACM (JACM)
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Automatically increasing the fault-tolerance of distributed algorithms
Journal of Algorithms
Upper and lower bounds for stochastic marked graphs
Information Processing Letters
Asynchronous consensus and broadcast protocols
Journal of the ACM (JACM)
The Byzantine Generals Problem
ACM Transactions on Programming Languages and Systems (TOPLAS)
Distributed Algorithms
Evaluating the running time of a communication round over the internet
Proceedings of the twenty-first annual symposium on Principles of distributed computing
IEEE Transactions on Parallel and Distributed Systems
Another advantage of free choice (Extended Abstract): Completely asynchronous agreement protocols
PODC '83 Proceedings of the second annual ACM symposium on Principles of distributed computing
Distributed Computing: Fundamentals, Simulations and Advanced Topics
Distributed Computing: Fundamentals, Simulations and Advanced Topics
Max-Plus Linear Stochastic Systems and Perturbation Analysis (The International Series on Discrete Event Dynamic Systems)
On the performance of a retransmission-based synchronizer
Theoretical Computer Science
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Designing algorithms for distributed systems that provide a round abstraction is often simpler than designing for those that do not provide such an abstraction. However, distributed systems need to tolerate various kinds of failures. The concept of a synchronizer deals with both: It constructs rounds and allows masking of transmission failures. One simple way of dealing with transmission failures is to retransmit a message until it is known that the message was successfully received. We calculate the exact value of the average rate of a retransmission-based synchronizer in an environment with probabilistic message loss, within which the synchronizer shows nontrivial timing behavior. The theoretic results, based on Markov theory, are backed up with Monte Carlo simulations.