scholarly journals Two-Round Diagnosability Measures for Multiprocessor Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Jiarong Liang ◽  
Qian Zhang ◽  
Changzhen Li
Keyword(s):  
Fault Diagnosis ◽  
Time Complexity ◽  
Multiprocessor System ◽  
Multiprocessor Systems ◽  
Pmc Model ◽  
Diagnosis Algorithm ◽  
Conditional Diagnosability ◽  
Novel Concept ◽  
Measure Index

In a multiprocessor system, as a key measure index for evaluating its reliability, diagnosability has attracted lots of attentions. Traditional diagnosability and conditional diagnosability have already been widely discussed. However, the existing diagnosability measures are not sufficiently comprehensive to address a large number of faulty nodes in a system. This article introduces a novel concept of diagnosability, called two-round diagnosability, which means that all faulty nodes can be identified by at most a one-round replacement (repairing the faulty nodes). The characterization of two-round t-diagnosable systems is provided; moreover, several important properties are also presented. Based on the abovementioned theories, for the n-dimensional hypercube Qn, we show that its two-round diagnosability is n2+n/2, which is n+1/2 times its classic diagnosability. Furthermore, a fault diagnosis algorithm is proposed to identify each node in the system under the PMC model. For Qn, we prove that the proposed algorithm is the time complexity of On2n.

Adaptive Diagnosis of Hamiltonian Networks under the Comparison Model

2021 ◽  
pp. 2150015
Author(s):  
Wenjun Liu ◽  
Wenjun Li
Keyword(s):  
Fault Diagnosis ◽  
Upper Bound ◽  
Multiprocessor Systems ◽  
Major Goal ◽  
Comparison Model ◽  
A Value ◽  
Pmc Model ◽  
Diagnosis Algorithm ◽  
Test Outcomes

Adaptive diagnosis is an approach in which tests can be scheduled dynamically during the diagnosis process based on the previous test outcomes. Naturally, reducing the number of test rounds as well as the total number of tests is a major goal of an efficient adaptive diagnosis algorithm. The adaptive diagnosis of multiprocessor systems under the PMC model has been widely investigated, while adaptive diagnosis using comparison model has been independently discussed only for three networks, including hypercube, torus, and completely connected networks. In addition, adaptive diagnosis of general Hamiltonian networks is more meaningful than that of special graph. In this paper, the problem of adaptive fault diagnosis in Hamiltonian networks under the comparison model is explored. First, we propose an adaptive diagnostic scheme which takes five to six test rounds. Second, we derive a dynamic upper bound of the number of fault nodes instead of setting a value like normal. Finally, we present an algorithm such that at least one sequence obtained from cycle partition can be picked out and all nodes in this sequence can be identified based on the previous upper bound.

Improved Precise Fault Diagnosis Algorithm for Hypercube-Like Systems Based on the Comparison Diagnosis Model

2016 ◽  
Vol 16 (03n04) ◽  
pp. 1650009 ◽  
Author(s):  
TAI-LING YE ◽  
DUN-WEI CHENG ◽  
SUN-YUAN HSIEH
Keyword(s):  
Fault Diagnosis ◽  
System Reliability ◽  
Time Complexity ◽  
Hamiltonian Cycle ◽  
High Reliability ◽  
Multiprocessor Systems ◽  
Ieee Transaction ◽  
Comparison Approach ◽  
Communication Links ◽  
Diagnosis Algorithm

Multiprocessor systems are being increasingly adopted and the system reliability is an important perspective for multiprocessor systems. The fault diagnosis has become crucial for achieving high reliability in multiprocessor systems. The precise fault diagnosis diagnoses all processors correctly. In the comparison-based model, it allows a processor to perform diagnosis by contrasting the responses from a pair of neighboring processors through sending the identical assignment. On the basis of comparison-based model, Sengupta and Dahbura (“On self-diagnosable multiprocessor systems: diagnosis by the comparison approach,” IEEE Transaction on Computers, vol. 41, no. 11, pp. 1386–1396, 1992) put forward the MM* model, any processor c diagnoses two processors c1 and c2 if c has direct communication links to them. Sengupta and Dahbura also designed an O(N5)-time precise fault diagnosis algorithm to diagnose faulty processors for general topologies by using the MM* model, where N is the cardinality of processor set in multiprocessor systems. Lately, Ye and Hsieh (“A scalable comparison-based diagnosis algorithm for hypercube-like net-works,” IEEE Transaction on Reliability, vol. 62, no. 4, pp. 789–799, 2013) devised an precise fault diagnosis algorithm to diagnose all faulty processors for hypercube-like networks by using the MM* model with O(N(log2N)2) time complexity. On the basis of Hamiltonian cycle properties, we improve the aforementioned results by presenting an O(N)-time precise fault diagnosis algorithm to diagnose all faulty processors for hypercube-like networks by using the MM* model.

scholarly journals The g-Good-Neighbor Diagnosability of Bubble-Sort Graphs under Preparata, Metze, and Chien’s (PMC) Model and Maeng and Malek’s (MM)* Model

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Shiying Wang ◽  
Zhenhua Wang
Keyword(s):  
Fault Diagnosis ◽  
Interconnection Networks ◽  
Multiprocessor System ◽  
Topology Structure ◽  
Free Node ◽  
Pmc Model ◽  
Good Neighbor

Diagnosability of a multiprocessor system is an important topic of study. A measure for fault diagnosis of the system restrains that every fault-free node has at least g fault-free neighbor vertices, which is called the g-good-neighbor diagnosability of the system. As a famous topology structure of interconnection networks, the n-dimensional bubble-sort graph B n has many good properties. In this paper, we prove that (1) the 1-good-neighbor diagnosability of B n is 2 n − 3 under Preparata, Metze, and Chien’s (PMC) model for n ≥ 4 and Maeng and Malek’s (MM) ∗ model for n ≥ 5 ; (2) the 2-good-neighbor diagnosability of B n is 4 n − 9 under the PMC model and the MM ∗ model for n ≥ 4 ; (3) the 3-good-neighbor diagnosability of B n is 8 n − 25 under the PMC model and the MM ∗ model for n ≥ 7 .

CONDITIONAL FAULT DIAGNOSABILITY OF DUAL-CUBES

2012 ◽  
Vol 23 (08) ◽  
pp. 1729-1747 ◽  
Author(s):  
SHUMING ZHOU ◽  
LANXIANG CHEN ◽  
JUN-MING XU
Keyword(s):  
Fault Diagnosis ◽  
High Reliability ◽  
Multiprocessor System ◽  
Connected Component ◽  
Comparison Model ◽  
Conditional Diagnosability ◽  
Almost All ◽  
Component Failures ◽  
Fault Diagnosability

The growing size of the multiprocessor system increases its vulnerability to component failures. It is crucial to locate and replace the faulty processors to maintain a system's high reliability. The fault diagnosis is the process of identifying faulty processors in a system through testing. This paper shows that the largest connected component of the survival graph contains almost all of the remaining vertices in the dual-cube DCn when the number of faulty vertices is up to twice or three times of the traditional connectivity. Based on this fault resiliency, this paper determines that the conditional diagnosability of DCn (n ≥ 3) under the comparison model is 3n − 2, which is about three times of the traditional diagnosability.

A Note on the Nature Diagnosability of Alternating Group Graphs Under the PMC Model and MM* Model

2018 ◽  
Vol 18 (01) ◽  
pp. 1850005 ◽  
Author(s):  
SHIYING WANG ◽  
LINGQI ZHAO
Keyword(s):  
Interconnection Networks ◽  
Interconnection Network ◽  
Alternating Group ◽  
Multiprocessor System ◽  
Multiprocessor Systems ◽  
Free Node ◽  
Pmc Model ◽  
Important Study ◽  
Communication Links ◽  
Alternating Group Graph

Many multiprocessor systems have interconnection networks as underlying topologies and an interconnection network is usually represented by a graph where nodes represent processors and links represent communication links between processors. No faulty set can contain all the neighbors of any fault-free node in the system, which is called the nature diagnosability of the system. Diagnosability of a multiprocessor system is one important study topic. As a favorable topology structure of interconnection networks, the n-dimensional alternating group graph AGn has many good properties. In this paper, we prove the following. (1) The nature diagnosability of AGn is 4n − 10 for n − 5 under the PMC model and MM* model. (2) The nature diagnosability of the 4-dimensional alternating group graph AG4 under the PMC model is 5. (3) The nature diagnosability of AG4 under the MM* model is 4.

Efficiently Handling Process Overruns and Underruns on Multiprocessors in Real-Time Embedded Systems

2017 ◽  
Author(s):  
Jia Xu
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Time Complexity ◽  
Multiprocessor System ◽  
Multiprocessor Systems ◽  
Worst Case ◽  
Real World Applications ◽  
Run Time ◽  
Hard Real Time ◽  
Time Overhead

In hard real-time and embedded multiprocessor system real-world applications, it is very important to strive to minimize the run-time overhead of the scheduler as much as possible, especially in hard real-time and embedded multiprocessor systems with limited processor and system resources. In this paper, we present a method that reduces the worst-case time complexity of the run-time scheduler for re-computing latest start times and for selecting processes for execution on a multiprocessor at run-time to O(n), where n is the number of processes.

scholarly journals The r-Extra Diagnosability of Hyper Petersen Graphs

2021 ◽  
pp. 1-12
Author(s):  
Shiying Wang
Keyword(s):  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Interconnection Networks ◽  
Interconnection Network ◽  
Petersen Graph ◽  
Multiprocessor System ◽  
Topology Structure ◽  
Pmc Model ◽  
Petersen Graphs ◽  
Text Model

The diagnosability of a multiprocessor system or an interconnection network plays an important role in measuring the fault tolerance of the network. In 2016, Zhang et al. proposed a new measure for fault diagnosis of the system, namely, the [Formula: see text]-extra diagnosability, which restrains that every fault-free component has at least [Formula: see text] fault-free nodes. As a famous topology structure of interconnection networks, the hyper Petersen graph [Formula: see text] has many good properties. It is difficult to prove the [Formula: see text]-extra diagnosability of an interconnection network. In this paper, we show that the [Formula: see text]-extra diagnosability of [Formula: see text] is [Formula: see text] for [Formula: see text] and [Formula: see text] in the PMC model and for [Formula: see text] and [Formula: see text] in the MM[Formula: see text] model.

scholarly journals The Component Diagnosability of General Networks

2021 ◽  
pp. 1-23
Author(s):  
Hongbin Zhuang ◽  
Wenzhong Guo ◽  
Xiaoyan Li ◽  
Ximeng Liu ◽  
Cheng-Kuan Lin
Keyword(s):  
Fault Diagnosis ◽  
Negative Impact ◽  
Rapid Expansion ◽  
Multiprocessor System ◽  
Multiprocessor Systems ◽  
Star Graphs ◽  
Comparison Results ◽  
Text Model ◽  
Text Component ◽  
General Networks

The processor failures in a multiprocessor system have a negative impact on its distributed computing efficiency. Because of the rapid expansion of multiprocessor systems, the importance of fault diagnosis is becoming increasingly prominent. The [Formula: see text]-component diagnosability of [Formula: see text], denoted by [Formula: see text], is the maximum number of nodes of the faulty set [Formula: see text] that is correctly identified in a system, and the number of components in [Formula: see text] is at least [Formula: see text]. In this paper, we determine the [Formula: see text]-component diagnosability of general networks under the PMC model and MM[Formula: see text] model. As applications, the component diagnosability is explored for some well-known networks, including complete cubic networks, hierarchical cubic networks, generalized exchanged hypercubes, dual-cube-like networks, hierarchical hypercubes, Cayley graphs generated by transposition trees (except star graphs), and DQcube as well. Furthermore, we provide some comparison results between the component diagnosability and other fault diagnosabilities.

A Linear Time Pessimistic Diagnosis Algorithm for Hypermesh Multiprocessor Systems under the PMC Model

2014 ◽  
Vol 63 (12) ◽  
pp. 2894-2904
Author(s):  
Hong-Chun Hsu ◽  
Kuang-Shyr Wu ◽  
Cheng-Kuan Lin ◽  
Chiou-Yng Lee ◽  
Chien-Ping Chang
Keyword(s):  
Linear Time ◽  
Multiprocessor Systems ◽  
Pmc Model ◽  
Diagnosis Algorithm
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