Fault-Containment in Weakly-Stabilizing Systems
SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
Algorithms and theory of computation handbook
A tranformational approach for designing scheduler-oblivious self-stabilizing algorithms
SSS'10 Proceedings of the 12th international conference on Stabilization, safety, and security of distributed systems
Dynamic FTSS in asynchronous systems: The case of unison
Theoretical Computer Science
SSS'11 Proceedings of the 13th international conference on Stabilization, safety, and security of distributed systems
Loosely-Stabilizing leader election in population protocol model
SIROCCO'09 Proceedings of the 16th international conference on Structural Information and Communication Complexity
Loosely-stabilizing leader election in a population protocol model
Theoretical Computer Science
Brief announcement: probabilistic stabilization under probabilistic schedulers
DISC'12 Proceedings of the 26th international conference on Distributed Computing
Towards scalable model checking of self-stabilizing programs
Journal of Parallel and Distributed Computing
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Self-stabilization is a strong property which guarantees that a network always resume a correct behavior starting from an arbitrary initial state. Weaker guarantees have later been introduced to cope with impossibility results: probabilistic stabilization only gives probabilistic convergence to a correct behavior. Also, weak-stabilization only gives the possibility of convergence. In this paper, we investigate the relative power of weak, self, and probabilistic stabilization, with respect to the set of problems that can be solved. We formally prove that in that sense, weak stabilization is strictly stronger that self-stabilization. Also, we refine previous results on weak stabilization to prove that, for practical schedule instances, a deterministic weak-stabilizing protocol can be turned into a probabilistic self-stabilizing one. This latter result hints at more practical use of weak-stabilization, as such algorithms are easier to design and prove than their (probabilistic) self-stabilizing counterparts.