Self-Organization for Fault-Tolerance
In the last decade, there has been a considerable increase of interest in fault-tolerant computing due to dependability problems related to process scaling, embedded systems, and ubiquitous computing. In this paper, we present an approach to fault-tolerance inspired by gene regulatory networks of living cells. Living cells are capable of maintaining their functionality under a variety of genetic changes and external perturbations. They have natural self-healing, self-maintaining, self-replicating, and self-assembling mechanisms. The fault-tolerance of living cells is due to the ability of their gene regulatory network to self-organize and produce a stable attractors’ landscape. We introduce a computational scheme which exploits the intrinsic stability of attractors to achieve fault-tolerant computation. We also test fault-tolerance of the presented scheme on the example of a gene regulatory network model of Arabidopsis thaliana and show that it can tolerate 68% single-point mutations in the outputs of the defining tables of gene functions.
KeywordsBoolean Function Gene Regulatory Network Ubiquitous Computing Boolean Network Soft Error
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