Time optimal self-stabilizing synchronization
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Impossibility of distributed consensus with one faulty process
Journal of the ACM (JACM)
Memory requirements for silent stabilization
PODC '96 Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing
Fault-containing self-stabilizing algorithms
PODC '96 Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing
The local detection paradigm and its applications to self-stabilization
Theoretical Computer Science
Fault-local distributed mending
Journal of Algorithms
Stabilizing time-adaptive protocols
Theoretical Computer Science
Self-stabilizing unidirectional network algorithms by power-supply
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Fault-containing self-stabilization using priority scheduling
Information Processing Letters
Self-stabilizing systems in spite of distributed control
Communications of the ACM
Distributed Algorithms
SIAM Journal on Computing
Journal of Parallel and Distributed Computing - Self-stabilizing distributed systems
Modeling Faults of Distributed, Reactive Systems
FTRTFT '00 Proceedings of the 6th International Symposium on Formal Techniques in Real-Time and Fault-Tolerant Systems
State-optimal snap-stabilizing PIF in tree networks
ICDCS '99 Workshop on Self-stabilizing Systems
Non-Exploratory Self-Stabilization for Constant-Space Symmetry-Breaking
ESA '94 Proceedings of the Second Annual European Symposium on Algorithms
Superstabilizing Protocols for Dynamic Distributed Systems
Superstabilizing Protocols for Dynamic Distributed Systems
Proceedings of the twenty-second annual symposium on Principles of distributed computing
Stability of long-lived consensus
Journal of Computer and System Sciences
Stabilization and pseudo-stabilization
Distributed Computing - Special issue: Self-stabilization
Guaranteed fault containment and local stabilization in routing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Adaptive stabilization of reactive protocols
FSTTCS'04 Proceedings of the 24th international conference on Foundations of Software Technology and Theoretical Computer Science
Average Binary Long-Lived Consensus: Quantifying the Stabilizing Role Played by Memory
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
Average long-lived binary consensus: Quantifying the stabilizing role played by memory
Theoretical Computer Science
Algorithms and theory of computation handbook
The optimal strategy for the average long-lived consensus
CSR'11 Proceedings of the 6th international conference on Computer science: theory and applications
Average long-lived memoryless consensus: the three-value case
SIROCCO'10 Proceedings of the 17th international conference on Structural Information and Communication Complexity
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Consider a network whose inputs change rapidly, or are subject to frequent faults. This is expected often to be the case in the foreseen huge sensor networks. Suppose, that an algorithm is required to output the majority value of the inputs. To address such networks, it is desirable to be able to stabilize the output fast, and to give guarantees on the outputs even before stabilization, even if additional changes occur. We bound the instability of the outputs (the number of times the output changes) of majority consensus algorithms even before the final stabilization. We show that the instability can be traded off with their time adaptvity (how fast they are required to stabilize the output if f faults occurred). First, for the extreme point of the trade-off, we achieve instability that is optimal for the class of algorithms that are optimal in their output time adaptivity. This is done for various known versions of majority consensus problem. The optimal instability for this case is Ω(log f) and is shown to be Ω(log f) for most versions and Ω(log n) in some cases. Previous such algorithms did not have such a guarantee on the behaviour of the output before its final stabilization (and their instability was Ω(n)). We also explain how to adapt the results for other points in the trade off. The output stabilization in previous algorithms was adaptive only if the faults ceased for O(Diam) time. An additional result in this paper uses adaptations of some previous tools, as well as the new tools developed here for bounding the instability, in order to remove this limitation that is undesirable when changes are frequent.