Stabilizing Communication Protocols
IEEE Transactions on Computers - Special issue on protocol engineering
A self-stabilizing algorithm for constructing spanning trees
Information Processing Letters
IEEE Transactions on Software Engineering
Closure and Convergence: A Foundation of Fault-Tolerant Computing
IEEE Transactions on Software Engineering - Special issue on software reliability
SuperStabilizing protocols for dynamic distributed systems
Proceedings of the fourteenth annual ACM symposium on Principles of distributed computing
Constraint satisfaction as a basis for designing nonmasking fault-tolerance
Journal of High Speed Networks
Fault-containing self-stabilizing algorithms
PODC '96 Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing
Stabilizing time-adaptive protocols
Theoretical Computer Science
State-optimal snap-stabilizing PIF in tree networks
ICDCS '99 Workshop on Self-stabilizing Systems
The Triumph and Tribulation of System Stabilization
WDAG '95 Proceedings of the 9th International Workshop on Distributed Algorithms
Alternating parallelism and the stabilization of distributed systems
Alternating parallelism and the stabilization of distributed systems
Self-stabilizing extensions for message-passing systems
Distributed Computing - Special issue: Self-stabilization
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We generalize the concept of stabilization of computing systems. According to this generalization, the actions of a system S are partitioned into n partitions, called phase 1 through phase n. In this case, system S is said to be n-stabilizing to a state predicate Q if S has state predicates P.0, ..., P.n such that P.0 = true, P.n = Q, and the following two conditions hold for every j, 1 = j = n. First, if S starts at a state satisfying P.(j-1) and if the only actions of S that are allowed to be executed are those of phase j or less, then S will reach a state satisfying P.j. Second, the set of states satisfying P.j is closed under any execution of the actions of phase j or less. By choosing n = 1, this generalization degenerates to the traditional definition of stabilization. We discuss three advantages of this generalization over the traditional definition. First, this generalization captures many stabilization properties of systems that are traditionally considered nonstabilizing. Second, verifying stabilization when n 1 is usually easier than when n = 1. Third, this generalization suggests a new method of fault recovery, called multiphase recovery.