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《多处理机系统可靠性分析》[44M]百度网盘|亲测有效|pdf下载

  • 多处理机系统可靠性分析

  • 出版社:科学出版社
  • 热度:8735
  • 上架时间:2024-06-30 09:08:33
  • 价格:0.0
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内容简介

本书通过一类基于有限群论中陪集理论构造的Cayley陪集图为拓扑结构的多处理机系统为实证对象，研究其基于群论的代数组合性质；有关点边连通度，限制连通度及其扩展的容错性质；基于经典的PMC测试模型和比较诊断模型下的各种条件诊断度；在局部诊断中引入概率策略，研究诊断策略的正确率，给出基于后验概率的局部和全局可靠性能分析；分别用组合概率和随机过程建立多处理机系统的子系统可靠性度量指标，并给出实证分析；通过计算子系统故障的平均到达时间来评价多处理机系统的可靠性。通过上述问题的探索揭示从微观角度下处理机的故障诊断度到宏观视野下多处理机系统的子系统的可靠性能分析的方法，以及离散问题连续化处理模式。

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目录

Contents
Preface
Chapter 1 Introduction 1
1.1 Fundamentals 1
1.1.1 Basic of Graph and Network 1
1.1.2 Progress in Reliability Evaluation 3
1.2 Fault Diagnostic Models 5
1.2.1 Diagnostic Model Based on Testing 5
1.2.2 Diagnostic Model Based on Comparison 8
1.3 Well-Known Network Topologies 10
1.3.1 Hypercube and Its Generalizations 11
1.3.2 Star Graph and Its Generalizations 22
Chapter 2 Fault Diagnosability of Star Graph 30
2.1 Fault Tolerance Properties of Star Graph 30
2.1.1 Introduction 30
2.1.2 Structural Robustness Properties of Star Graph 31
2.2 Conditional Diagnosability of Star Graph 41
2.2.1 Introduction 41
2.2.2 Preliminaries 42
2.2.3 Conditional Diagnosability of Star Graph 42
2.3 g-Extra Diagnosability of Star Graph 56
2.3.1 Introduction 57
2.3.2 g-Extra Connectivity of Star Graph 58
2.3.3 g-Extra Diagnosability of Star Graph 68
2.4 t=k-Diagnosability of Star Graph 72
2.4.1 Introduction 73
2.4.2 t=k-Diagnosability of Star Graph 75
2.4.3 Review on Related Works 78
2.5 Remarks 80
Chapter 3 Fault Diagnosability of (n，k)-Star Graph 82
3.1 Conditional Diagnosability of (n，k)-Star Graph 82
3.1.1 Introduction 82
3.1.2 Structural Robustness of (n，k)-Star Graph 83
3.1.3 Conditional Diagnosability of (n，k)-Star Graph 91
3.2 g-Good-Neighbor Diagnosability of (n，k)-Star Graph 93
3.2.1 Introduction 94
3.2.2 g-Good-Neighbor Diagnosability of (n，k)-Star Graph Under the PMC Model 95
3.2.3 g-Good-Neighbor Diagnosability of (n，k)-Star Graph Under the Comparison Model 99
3.3 g-Extra Diagnosability of (n，k)-Star Graph 105
3.3.1 Introduction 106
3.3.2 g-Extra Connectivity of (n，k)-Star Graph 107
3.3.3 g-Extra Conditional Diagnosability of (n，k)-Star Graph 112
3.4 Remarks 117
Chapter 4 Reliability Analysis Based on Subsystems of (n，k)-Star Graph 119
4.1 Introduction 120
4.2 Preliminaries 121
4.3 Evaluation of Rn-1，k-1 n，k(p) 123
4.3.1 Computation of Lower Bound on Rn-1，k-1 n，k(p) 123
4.3.2 Computation of Upper Bound on Rn-1，k-1 n，k(p) 134
4.3.3 Approximation Rn-1，k-1 n，k(p) 134
4.3.4 Discussion 136
4.4 Remarks 138
Chapter 5 Reliability Assessment of Multiprocessor System Based on (n，k)-Star Graph 139
5.1 Introduction 139
5.2 Reliability Analysis Under Node Fault Model 140
5.2.1 Partition Based on Fixed Dimension 141
5.2.2 Partition Based on Liberal Dimension 151
5.3 Reliability Analysis Under Link Fault Model 154
5.3.1 Partition Based on Fixed Dimension 154
5.3.2 Partition Based on Liberal Dimension 156
5.4 Remarks 158
References 159

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前言/序言

　　As the world moves rapidly toward the era of cloud computing， high-speed， large-scale multiprocessor and multicomputer systems are bound to gain more popularity.Many large-scale multiprocessor/multicomputer systems use interconnection net-works of various topologies to connect processors/computers. With the increase in scalability and application of multiprocessor system， the components of the system are prone to failures， so the comprehensive exploration on modeling and analysis of fault detection as well as fault tolerance is becoming more and more vital. The qualitative and quantitative reliability analysis relies on appropriate mathematical modeling and analysis technique. Analytical performance modeling is the key to studying the reliability of multiprocessor systems， whose underlying interconnection networks are always modelled by graphs.
　　This monograph consists of five chapters. The first chapter introduces basic con-cepts and notations in graph theory and combinatorial network theory， the major system-level fault diagnostic models， and some network topologies worthy of study.Structural robustness evaluation and fault diagnosability of 8tar graph and its gen-eralization， （n.，k）-star graph， under distinct diagnostic models and strategies， are studied in Chapters 2 and 3. Chapter 4 is devoted to the methodology from the per-spective of fault probability of units on the microscale to that of subsystem-based reliability of multiprocessor sy8tems on the macroscale. More specifically， using a combinatorial probabilistic model， we explore the theoretical upper and lower bounds on the subsystem reliability of （n， k）-star graphs， which are shown to be in good agreement with the empirical numerical results， especially when the node reli-
　　ability p goes low. The last chapter is committed to the idea of discrete-continuous solution model. The significance of establishing the reliability of （n.，k）-star graph is that it can be used as a good indicator of the health of the entire system in the presence of node and/or link failure. In this chapter， we use the mean time to failure （simply， MTTF） to measure the reliability of the multiprocessor system based on （n.， k）-star graph. Differential equations and matrix theory are used to compute the MTTF under fixed partition and re-mapping partition. By comparing the MTTF under those two models， we show that the multiprocessor system based on （n，，k）-star graph has better robustness under the re-mapping partition model， whether for node failure or link failure. That is， the re-mapping partition model can do better to refiect the reliability of （n， k）-star graph.
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