Affiliation:
1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, School of Computer Science and Mathematics, Fujian University of Technology, Fujian, China
2. College of Computer and Cyber Security, Fujian Normal University, Fujian, China
3. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
Abstract
The fault diagnosability of a network indicates the self-diagnosis ability of the network, thus it is an important measure of robustness of the network. As a neoteric feature for measuring fault diagnosability, the
r
-component diagnosability
ct
r
(G)
of a network
G
imposes the restriction that the number of components is at least
r
in the remaining network of
G
by deleting faulty set
X
, which enhances the diagnosability of
G
. In this article, we establish the
r
-component diagnosability for
n
-dimensional hierarchical cubic network
HCN
n
, and we show that, under both PMC model and MM* model, the
r
-component diagnosability of
HCN
n
is
rn
-½(
r
-1)
r
+1 for
n
≥ 2 and
1≤ r≤ n-1
. Moreover, we introduce the concepts of 0-PMC subgraph and 0-MM* subgraph of
HCN
n
. Then, we make use of 0-PMC subgraph and 0-MM* subgraph of
HCN
n
to design two algorithms under PMC model and MM* model, respectively, which are practical and efficient for component fault diagnosis of
HCN
n
. Besides, we compare the
r
-component diagnosability of
HCN
n
with the extra conditional diagnosability, diagnosability, good-neighbor diagnosability, pessimistic diagnosability, and conditional diagnosability, and we verify that the
r
-component diagnosability of
HCN
n
is higher than the other types of diagnosability.
Funder
National Natural Science Foundation of China
Fok Ying Tung Education Foundation
Natural Science Foundation of Fujian Province
Fujian University of Technology
Publisher
Association for Computing Machinery (ACM)
Subject
Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications
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