Atjaunināt sīkdatņu piekrišanu

Performance, Reliability and Availability Evaluation of Computational Systems [Multiple-component retail product]

  • Formāts: Multiple-component retail product, 1656 pages, height x width: 280x210 mm, weight: 3280 g, 330 Tables, black and white; 647 Line drawings, black and white; 69 Halftones, black and white; 716 Illustrations, black and white, Contains 2 hardbacks
  • Izdošanas datums: 06-Apr-2023
  • Izdevniecība: CRC Press
  • ISBN-10: 1032325771
  • ISBN-13: 9781032325774
Citas grāmatas par šo tēmu:
  • Multiple-component retail product
  • Cena: 262,79 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Performance, Reliability and Availability Evaluation of Computational SystemsPalielināt
  • Formāts: Multiple-component retail product, 1656 pages, height x width: 280x210 mm, weight: 3280 g, 330 Tables, black and white; 647 Line drawings, black and white; 69 Halftones, black and white; 716 Illustrations, black and white, Contains 2 hardbacks
  • Izdošanas datums: 06-Apr-2023
  • Izdevniecība: CRC Press
  • ISBN-10: 1032325771
  • ISBN-13: 9781032325774
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781032325774.html
Keywords:

This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance, reliability, and availability evaluation subjects. This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance, reliability, and availability evaluation subjects.



This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance, reliability, and availability evaluation subjects. The volumes focus on computing systems, although the methods may also be applied to other systems. The first volume covers Chapter 1 to Chapter 14, whose subtitle is ``Performance Modeling and Background". The second volume encompasses Chapter 15 to Chapter 25 and has the subtitle ``Reliability and Availability Modeling, Measuring and Workload, and Lifetime Data Analysis".

 

This text is helpful for computer performance professionals for supporting planning, design, configuring, and tuning the performance, reliability, and availability of computing systems. Such professionals may use these volumes to get acquainted with specific subjects by looking at the particular chapters. Many examples in the textbook on computing systems will help them understand the concepts covered in each chapter. The text may also be helpful for the instructor who teaches performance, reliability, and availability evaluation subjects. Many possible threads could be configured according to the interest of the audience and the duration of the course. Chapter 1 presents a good number of possible courses programs that could be organized using this text.

Preface xiii
Acknowledgement xv
Chapter 1 Introduction
1(24)
1.1 An Overview
1(8)
1.2 A Glance at Evaluation Planning
9(16)
PART I Fundamental Concepts
Chapter 2 Introduction to Probability
25(30)
2.1 Sets and Algebra of Sets
25(4)
2.2 Probability
29(5)
2.3 Conditional Probability
34(5)
2.4 Independence
39(1)
2.5 Bayes' Rule and the Law of Total Probability
40(5)
2.6 Counting
45(10)
2.6.1 N-Permutation
45(2)
2.6.2 K out of N Permutation with Replacement
47(1)
2.6.3 K out of N Permutation without Replacement
48(1)
2.6.4 K out of N Combination without Replacement
48(1)
2.6.5 K out of N Combination with Replacement
49(6)
Chapter 3 Exploratory Data Analysis
55(24)
3.1 Diagrams and Plots
55(7)
3.2 Statistics of Central Tendency
62(4)
3.3 Measures of Dispersion
66(2)
3.4 Statistics of Shape (Asymmetry and Kurtosis)
68(2)
3.5 Outliers
70(9)
Chapter 4 Introduction to Random Variables
79(60)
4.1 Discrete Random Variables
79(11)
4.2 Continuous Random Variables
90(8)
4.3 Moments
98(12)
4.4 Joint Distributions
110(21)
4.4.1 Joint Discrete Random Variables
111(4)
4.4.2 Joint Continuous Random Variables
115(5)
4.4.3 Convolution
120(4)
4.4.4 Expect, and Var. of Prod, of Rand. Variab
124(4)
4.4.5 Expect, and Var. of Sums of Rand. Variab
128(3)
4.5 Summary of Properties of Expectation and Variance
131(1)
4.6 Covariance, Correlation, and Independence
132(7)
Chapter 5 Some Important Random Variables
139(80)
5.1 Some Discrete Random Variables
139(17)
5.1.1 Bernoulli
139(2)
5.1.2 Geometric
141(3)
5.1.3 Binomial
144(3)
5.1.4 Negative Binomial
147(3)
5.1.5 Hypergeometric
150(2)
5.1.6 Poisson
152(4)
5.2 Some Continuous Random Variables
156(46)
5.2.1 Uniform
156(2)
5.2.2 Triangular
158(2)
5.2.3 Normal
160(5)
5.2.4 Chi-Square
165(3)
5.2.5 Student's
168(3)
5.2.6 F Distributions
171(3)
5.2.7 Exponential
174(4)
5.2.8 Gamma
178(3)
5.2.9 Phase-Type
181(1)
5.2.10 Erlang
181(4)
5.2.11 Hypoexponential
185(5)
5.2.12 Hyperexponential
190(3)
5.2.13 Cox
193(4)
5.2.14 Weibull
197(5)
5.3 Functions of a Random Variable
202(5)
5.4 Taylor Series
207(12)
Chapter 6 Statistical Inference and Data Fitting
219(76)
6.1 Parametric Confidence Interval for Mean
219(9)
6.1.1 Confidence Interval when Variance is Known
221(4)
6.1.2 Confidence Interval when Variance is Unknown
225(3)
6.2 Parametric Confidence Interval for SD2 and SD
228(3)
6.3 Parametric Confidence Interval for Proportion
231(6)
6.3.1 Parametric Confid. Interv. for p based on b(n, k)
231(4)
6.3.2 Parametric Confid. Interv. for p based on N(μ, σ)
235(2)
6.4 Parametric Confidence Interval for Difference
237(9)
6.4.1 Confidence Interval for Paired Comparison
237(4)
6.4.2 Conf. Interv. for Non-Corresp. Measurements
241(5)
6.5 Bootstrap
246(9)
6.5.1 Basic Bootstrap
246(3)
6.5.2 Bootstrap-t
249(3)
6.5.3 Semi-Parametric Bootstrap
252(3)
6.6 Goodness of Fit
255(13)
6.6.1 Probability-Probability Plot Method
255(3)
6.6.2 Χ2 Method
258(4)
6.6.3 Kolmogorov-Smirnov Method
262(6)
6.7 Data Fitting
268(27)
6.7.1 Linear Regression
268(8)
6.7.2 Polynomial Regression
276(3)
6.7.3 Exponential Regression
279(3)
6.7.4 Lagrange's Polynomial
282(13)
Chapter 7 Data Scaling, Distances, and Clustering
295(70)
7.1 Data Scaling
295(18)
7.2 Distance and Similarity Measures
313(5)
7.3 Cluster Distances
318(5)
7.4 Clustering: an introduction
323(7)
7.5 K-Means
330(7)
7.6 K-Medoid and K-Median
337(4)
7.7 Hierarchical Clustering
341(24)
PART II Performance Modeling
Chapter 8 Operational Analysis
365(28)
8.1 Utilization Law
367(1)
8.2 Forced Flow Law
368(1)
8.3 Demand Law
369(3)
8.4 Little's Law
372(4)
8.5 General Response Time Law
376(2)
8.6 Interactive Response Time Law
378(4)
8.7 Bottleneck Analysis and Bounds
382(11)
Chapter 9 Discrete Time Markov Chain
393(46)
9.1 Stochastic Processes
393(5)
9.2 Chapman-Kolmogorov Equation
398(6)
9.3 Transient Distribution
404(3)
9.4 Steady State Distribution
407(1)
9.5 Classification of States, MRT and MFPT
408(13)
9.6 Holding Time (Sojourn Time or Residence Time)
421(2)
9.7 Mean Time to Absorption
423(2)
9.8 Some Applications
425(14)
Chapter 10 Continuous Time Markov Chain
439(86)
10.1 Rate Matrix
439(3)
10.2 Chapman-Kolmogorov Equation
442(6)
10.3 Holding Times
448(2)
10.4 Stationary Analysis
450(13)
10.4.1 Gauss Elimination
452(6)
10.4.2 Gauss-Seidel Method
458(5)
10.5 Transient Analysis
463(17)
10.5.1 Interval Subdivision
464(1)
10.5.2 First Order Differential Linear Equation
464(5)
10.5.3 Solution through Laplace Transform
469(3)
10.5.4 Uniformization Method
472(8)
10.6 Time to Absorption
480(8)
10.6.1 Method Based on Moments
485(3)
10.7 Semi-Markov Chain
488(3)
10.8 Additional Modeling Examples
491(34)
10.8.1 Online Processing Request Control
492(1)
10.8.2 Tiny Private Cloud System
493(2)
10.8.3 Two Servers with Different Processing Rates
495(5)
10.8.4 M/E/1/4 Queue System
500(3)
10.8.5 Mobile Application Offloading
503(3)
10.8.6 Queue System with MMPP Arrival
506(3)
10.8.7 Poisson Process and Two Queues
509(3)
10.8.8 Two Stage Tandem System
512(3)
10.8.9 Event Recommendation Mashup
515(10)
Chapter 11 Basic Queueing Models
525(38)
11.1 The Birth and Death Process
525(2)
11.2 M/M/1 Queue
527(9)
11.3 M/M/m Queue
536(9)
11.4 M/M/∞ Queue
545(4)
11.5 M/M/mk Queue
549(5)
11.6 M/Mmk Queue
554(5)
11.7 M/Mrnm Queue
559(4)
Chapter 12 Petri Nets
563(56)
12.1 A Glance at History
563(1)
12.2 Basic Definitions
564(9)
12.3 Basic Models
573(2)
12.4 Conflict, Concurrency, and Confusion
575(6)
12.5 Petri Nets Subclasses
581(3)
12.6 Modeling Classical Problems
584(11)
12.7 Behavioral Properties
595(5)
12.7.1 Boundedness
596(1)
12.7.2 Reachability
597(1)
12.7.3 Reversibility
597(1)
12.7.4 Conservation
598(1)
12.7.5 Deadlock Freedom
598(1)
12.7.6 Liveness
599(1)
12.7.7 Coverability
600(1)
12.8 Behavioral Property Analysis
600(8)
12.8.1 Coverability Tree
600(1)
12.8.2 State Equation
601(5)
12.8.3 Reductions
606(2)
12.9 Structural Properties and Analysis
608(11)
12.9.1 Transition Invariants
609(2)
12.9.2 Place Invariants
611(8)
Chapter 13 Stochastic Petri Nets
619(86)
13.1 Definition and Basic Concepts
619(15)
13.1.1 A Comment about the Model Presented
633(1)
13.2 Mapping SPN to CTMC
634(8)
13.3 Performance Modeling with SPN
642(63)
13.3.1 M/M/1/k Queue System
643(4)
13.3.2 Modulated Traffic
647(1)
13.3.3 M/M/m/k Queue System
648(6)
13.3.4 Queue System with Distinct Classes of Stations
654(5)
13.3.5 Queue System with Breakdown
659(1)
13.3.6 Queue System with Priority
660(2)
13.3.7 Open Tandem Queue System with Blocking
662(5)
13.3.8 Modeling Phase-Type Distributions
667(23)
13.3.9 Memory Policies and Phase-Type Distributions
690(5)
13.3.10 Probability Distribution of SPNs
695(10)
Chapter 14 Stochastic Simulation
705(1)
14.1 Introduction
705(1)
14.1.1 Monte Carlo Simulation
706(7)
14.2 Discrete Event Simulation: an Overview
713(9)
14.3 Random Variate Generation
722(1)
14.3.1 Pseudo-Random Number Generation
722(4)
14.3.2 Inverse Transform Method
726(9)
14.3.3 Convolution Method
735(3)
14.3.4 Composition Method
738(1)
14.3.5 Acceptance-Rejection Method
739(3)
14.3.6 Characterization
742(3)
14.4 Output Analysis
745(32)
14.4.1 Transient Simulation
747(19)
14.4.2 Steady-State Simulation
766(11)
14.5 Additional Modeling Examples
777(1)
14.5.1 G/G/m Queue System
777(3)
14.5.2 G/G/m Queue System with Breakdown
780(2)
14.5.3 Planning Mobile Cloud Infrastructures
782(5)
Bibliography 787
Preface xiii
Acknowledgement xv
Chapter 15 Introduction
1(14)
PART III Reliability and Availability Modeling
Chapter 16 Fundamentals of Dependability
15(28)
16.1 A Brief History
15(2)
16.2 Fundamental Concepts
17(13)
16.3 Some Important Probability Distributions
30(13)
Chapter 17 Redundancy
43(20)
17.1 Hardware Redundancy
44(15)
17.2 Software Redundancy
59(4)
Chapter 18 Reliability Block Diagram
63(76)
18.1 Models Classification
63(1)
18.2 Basic Components
63(2)
18.3 Logical and Structure Functions
65(5)
18.4 Coherent System
70(1)
18.5 Compositions
70(33)
18.6 System Redundancy and Component Redundancy
103(3)
18.7 Common Cause Failure
106(1)
18.8 Paths and Cuts
107(1)
18.9 Importance Indices
108(31)
Chapter 19 Fault Tree
139(32)
19.1 Components of a Fault Tree
139(5)
19.2 Basic Compositions
144(10)
19.3 Compositions
154(13)
19.4 Common Cause Failure
167(4)
Chapter 20 Combinatorial Model Analysis
171(42)
20.1 Structure Function Method
171(1)
20.2 Enumeration Method
172(8)
20.3 Factoring Method
180(4)
20.4 Reductions
184(5)
20.5 Inclusion-Exclusion Method
189(6)
20.6 Sum of Disjoint Products Method
195(5)
20.7 Methods for Estimating Bounds
200(13)
20.7.1 Method Based on Inclusion and Exclusion
200(3)
20.7.2 Method Based on the Sum of Disjoint Products
203(2)
20.7.3 Min-Max Bound Method
205(1)
20.7.4 Esary-Proschan Method
206(1)
20.7.5 Decomposition
207(6)
Chapter 21 Modeling Availability, Reliability, and Capacity with CTMC
213(62)
21.1 Single Component
213(3)
21.2 Hot-Standby Redundancy
216(7)
21.3 Hot-Standby with Non-Zero Delay Switching
223(4)
21.4 Imperfect Coverage
227(6)
21.5 Cold-Standby Redundancy
233(4)
21.6 Warm-Standby Redundancy
237(6)
21.7 Active-Active Redundancy
243(10)
21.8 Many Similar Machines with Repair Facilities
253(7)
21.9 Many Similar Machines with Shared Repair Facility
260(2)
21.10 Phase-Type Distribution and Preventive Maintenance
262(3)
21.11 Two-States Availability Equivalent Model
265(3)
21.12 Common Cause Failure
268(7)
Chapter 22 Modeling Availability, Reliability, and Capacity with SPN
275(72)
22.1 Single Component
275(2)
22.2 Modeling TTF and TTR with Phase-Type Distribution
277(4)
22.3 Hot-Standby Redundancy
281(3)
22.4 Imperfect Coverage
284(5)
22.5 Cold-Standby Redundancy
289(3)
22.6 Warm-Standby Redundancy
292(3)
22.7 Active-Active Redundancy
295(2)
22.8 KooN Redundancy
297(8)
22.8.1 Modeling Multiple Resources on Multiple Servers
299(6)
22.9 Corrective Maintenance
305(5)
22.10 Preventive Maintenance
310(7)
22.11 Common Cause Failure
317(1)
22.12 Some Additional Models
318(29)
22.12.1 Data Center Disaster Recovery
318(7)
22.12.2 Disaster Tolerant Cloud Systems
325(8)
22.12.3 MHealth System Infrastructure
333(14)
PART IV Measuring and Data Analysis
Chapter 23 Performance Measuring
347(98)
23.1 Basic Concepts
347(4)
23.2 Measurement Strategies
351(1)
23.3 Basic Performance Metrics
352(1)
23.4 Counters and Timers
353(6)
23.5 Measuring Short Time Intervals
359(5)
23.6 Profiling
364(12)
23.6.1 Deterministic Profiling
364(5)
23.6.2 Statistical Profiling
369(7)
23.7 Counters and Basic Performance Tools in Linux
376(55)
23.7.1 System Information
377(36)
23.7.2 Process Information
413(18)
23.8 Final Comments
431(14)
Chapter 24 Workload Characterization
445(82)
24.1 Types of Workloads
446(5)
24.2 Workload Generation
451(26)
24.2.1 Benchmarks
451(17)
24.2.2 Synthetic Operational Workload Generation
468(9)
24.3 Workload Modeling
477(50)
24.3.1 Modeling Workload Impact
478(15)
24.3.2 Modeling Intended Workload
493(34)
Chapter 25 Lifetime Data Analysis
527(86)
25.1 Introduction
527(7)
25.1.1 Reliability Data Sources
528(4)
25.1.2 Censoring
532(2)
25.2 Non-Parametric Methods
534(23)
25.2.1 Ungrouped Complete Data Method
535(4)
25.2.2 Grouped Complete Data Method
539(3)
25.2.3 Ungrouped Multiply Censored Data Method
542(3)
25.2.4 Kaplan-Meier Method
545(12)
25.3 Parametric Methods
557(56)
25.3.1 Graphical Methods
558(11)
25.3.2 Method of Moments
569(8)
25.3.3 Maximum Likelihood Estimation
577(18)
25.3.4 Confidence Intervals
595(18)
Chapter 26 Fault Injection and Failure Monitoring
613(28)
26.1 Fault Acceleration
614(11)
26.2 Some Notable Fault Injection Tools
625(7)
26.3 Software-Based Fault Injection
632(9)
Bibliography 641(40)
Appendix A MTTF 2oo5 681(2)
Appendix B Whetsone 683(10)
Appendix C Linpack-Bench 693(24)
Appendix D Livermore Loops 717(12)
Appendix E MMP - CTMC Trace Generator 729
Paulo Romero Martins Maciel is Full Professor at Centro de Informįtica da Universidade Federal de Pernambuco (UFPE), Brazil.