“…One of the major issues in fault tolerance of a multiprocessor system is fault diagnosis, which is to identify the faulty processors in the system. Several models for self-diagnosis of a system have been proposed [7,17,18].…”
Section: Introductionmentioning
confidence: 99%
“…if (n, k 1 , k 2 ) = (2, 3, 4), 7 if (n, k 1 , k 2 ) = (2, 4, 4), 8n − 7 if k 1 ≥ 4, (n, k 1 , k 2 ) (2, 4, 4), 8n − 9 if k 1 = 3 & k 2 ≥ 4, (n, k 1 , k 2 ) (2, 3, 4), 8n − 11 if k 1 = k 2 = 3, (n, k 1 , k 2 ) (2, 3, 3).…”
A general technique is proposed for determining the conditional diagnosability of interconnection networks under the PMC model. Several graph invariants are involved in the approach, such as the length of the shortest cycle, the minimum number of neighbors, γ p (resp. γ p ), over all p-vertex subsets (resp. cycles), and a variant of connectivity, called the r-super-connectivity. An n-dimensional torus network is defined as a Cartesian product of n cycles, C k 1 ×· · ·× C k n , where C k j is a cycle of length k j for 1 ≤ j ≤ n. The proposed technique is applied to the two or higher-dimensional torus networks, and their conditional diagnosabilities are established completely: the conditional diagnosability of every torus network G is equal to γ 4 (G) + 1, excluding the three small ones C 3 × C 3 , C 3 × C 4 , and C 4 × C 4 . In addition, γ p (G) as well as γ 4 (G) is derived for 2 ≤ p ≤ 4 and the r-super-connectivity is also derived for 1 ≤ r ≤ 3 .
“…One of the major issues in fault tolerance of a multiprocessor system is fault diagnosis, which is to identify the faulty processors in the system. Several models for self-diagnosis of a system have been proposed [7,17,18].…”
Section: Introductionmentioning
confidence: 99%
“…if (n, k 1 , k 2 ) = (2, 3, 4), 7 if (n, k 1 , k 2 ) = (2, 4, 4), 8n − 7 if k 1 ≥ 4, (n, k 1 , k 2 ) (2, 4, 4), 8n − 9 if k 1 = 3 & k 2 ≥ 4, (n, k 1 , k 2 ) (2, 3, 4), 8n − 11 if k 1 = k 2 = 3, (n, k 1 , k 2 ) (2, 3, 3).…”
A general technique is proposed for determining the conditional diagnosability of interconnection networks under the PMC model. Several graph invariants are involved in the approach, such as the length of the shortest cycle, the minimum number of neighbors, γ p (resp. γ p ), over all p-vertex subsets (resp. cycles), and a variant of connectivity, called the r-super-connectivity. An n-dimensional torus network is defined as a Cartesian product of n cycles, C k 1 ×· · ·× C k n , where C k j is a cycle of length k j for 1 ≤ j ≤ n. The proposed technique is applied to the two or higher-dimensional torus networks, and their conditional diagnosabilities are established completely: the conditional diagnosability of every torus network G is equal to γ 4 (G) + 1, excluding the three small ones C 3 × C 3 , C 3 × C 4 , and C 4 × C 4 . In addition, γ p (G) as well as γ 4 (G) is derived for 2 ≤ p ≤ 4 and the r-super-connectivity is also derived for 1 ≤ r ≤ 3 .
“…The comparison-based models, proposed initially by Malek [12], and by Chwa and Hakimi [13], have been considered to be a practical approach for fault diagnosis in distributed systems. In these first comparison-based models, it was assumed that system tasks are duplicated on two distinct units in the system and their outputs are compared by a central observer.…”
“…Models for comparisonbased system-level diagnosis were initially proposed by Malek [11], and by Chwa and Hakimi [12]. These models assume that, in a system of N units, the comparison of the outputs produced by any pair of units is possible.…”
Section: Introductionmentioning
confidence: 99%
“…The difference between these two models is that in [12] two faulty units can produce the same output for a task, so that the comparison of these outputs results in a match.…”
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