Title
On the complexity of adding convergence
Document Type
Conference Proceeding
Publication Date
1-1-2013
Abstract
This paper investigates the complexity of designing Self-Stabilizing (SS) distributed programs, where an SS program meets two properties, namely closure and convergence. Convergence requires that, from any state, the computations of an SS program reach a set of legitimate states (a.k.a. invariant). Upon reaching a legitimate state, the computations of an SS program remain in the set of legitimate states as long as no faults occur; i.e., Closure. We illustrate that, in general, the problem of augmenting a distributed program with convergence, i.e., adding convergence, is NP-complete (in the size of its state space). An implication of our NP-completeness result is the hardness of adding nonmasking fault tolerance to distributed programs, which has been an open problem for the past decade. © 2013 IFIP International Federation for Information Processing.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Klinkhamer, A.,
&
Ebnenasir, A.
(2013).
On the complexity of adding convergence.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
8161 LNCS, 17-33.
http://doi.org/10.1007/978-3-642-40213-5_2
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/4186