实例介绍
【实例简介】
Amazon上的5星级图书，对优化系统非常有帮助
6.8.7 Clustering Techniques 6.8.8 Minimum Spanning Tree Method 6.8.9 Cluster Interpretation 6.8.10 Problems with clustering EXERCISES CHAPTER7— MONITORS 7.1 MONITOR TERMINOLOGY 7.2 MONITOR CLASSIFICATION 7. 3 SOFTWARE MONITORS 7.3.1 Issues In Software Monitor Design 7. 4 HARDWARE MONITORS 7.5 SOFTWARE VERSUS HARDWARE MONITORS 7.6 FIRMWARE AND HYBRID MONITORS 7.7 DISTRIBUTED-SYSTEM MONITORS 77。1 Observation 7.7.2 Collection 7.7.3 Analysis 7.7.4 Presentation 7.7.5 Interpretation 7.7.6 Console Functions EXERCISES chAPTER8-PROGRAM EXECUTION MONITORS AND ACCOUNTING LOGS 8.1 PROGRAM EXECUTION MONITORS 8.1.1 Issues In designing a program execution monitor 8.2 TECHNIQUES FOR IMPROVING PROGRAM PERFORMANCE 8. 3 ACCOUNTING LOGS 8. 4 ANALYSIS AND INTERPRETATION OF ACCOUNTING LOG DATA 8.5 USING ACCOUNTING LOGS TO ANSWER COMMONLY ASKED QUESTIONS EXERCISE ChAPTER 9-CAPACITY PLANNING AND BENCHMARKING 9.1 STEPS IN CAPACITY PLANNING AND MANAGEMENT 9.2 PROBLEMS IN CAPACITY PLANNING 9. 3 COMMON MISTAKES IN BENCHMARKING 9. 4 BENCHMARKING GAMES 9.5 LOAD DRIVERS 9.6 REMOTE-TERMIINAL EMULATION 9.7 COMPONENTS OF AN RTE 9. 8 LIMITATIONS OF CURRENT RTES EXERCISES CHAPTER 10-THE ART OF DATA PRESENTATION 10.1 TYPES OF VARIABLES 10.2 GUIDELINES FOR PREPARING GOOD GRAPHIC CHARTS 10.3 COMMON MISTAKES IN PREPARING CHARTS 10.4 PICTORIAL GAMES 10.5 GANTT CHARTS 10.6 KIVIAT GRAPHS 10.6.1 Shapes of Kiviat Graphs 10.6.2 Application of Kiviat Graphs to Other Systems 10.7 SCHUMACHER CHARTS 10. 8 DECISION MAKERS GAMES CHAPTER 11-RATIO GAMES 11.1 CHOOSING AN APPROPRIATE BASE SYSTEM 11.2 USING AN APPROPRIATE RATIO METRIC 11.3 USING RELATIVE PERFORMANCE ENHANCEMENT 11. 4 RATIO GAMES WITH PERCENTAGES 11.5 STRATEGIES FOR WINNING A RATIO GAME 11.6 CORRECT ANALYSIS EXERCISES FURTHER READING FOR PART II PART IIL-PROBABILITY THEORY AND STATISTICS CHAPTER 12-SUMMARIZING MEASURED DATA 12.1 BASIC PROBABILITY AND STATISTICS CONCEPTS 12.2 SUMMARIZING DATA BY A SINGLE NUMBER 2.3 SELECTING AMONG THE MEAN MEDIAN AND MODE 12.4 COMMON MISUSES OF MEANS 12.5 GEOMETRIC MEAN 12.6 HARMONIC MEAN 12.7 MEAN OF A RATIO 12. 8 SUMMARIZING VARIABILITY 12.9 SELECTING THE INDEX OF DISPERSION 12.10 DETERMINING DISTRIBUTION OF DATA EXERCISES CHAPTER 13-COMPARING SYSTEMS USING SAMPLE DATA 13.1 SAMPLE VERSUS POPULATION 13.2 CONFIDENCE INTERVAL FOR THE MEAN 13.3 TESTING FOR A ZERO MEAN 13. 4 COMPARING TWO ALTERNATIVES 13. 4.1 Paired observations 13.4.2 Unpaired observations 13.4.3 Approximate visual tes 13.5 WHAT CONFIDENCE LEVEL TO USE 13.6 HYPOTHESIS TESTING VERSUS CONFIDENCE INTERVALS 13.7 ONE-SIDED CONFIDENCE INTERVALS 13. 8 CONFIDENCE INTERVALS FOR PROPORTIONS 13.9 DETERMINING SAMPLE SIZE 13.9.1 Sample Size for Determining Mean 13.9.2 Sample size for Determining proportions 13.9.3 Sample Size for Comparing Two Alternatives CHAPTER 14SIMPLE LINEAR REGRESSION MODELS 14.1 DEFINITION OF A GOOD MODEL 14.2 ESTIMATION OF MODEL PARAMETERS 14.5 CONFIDENCE INTERVALS FOR REGRESSION PARAMETERS 14.6 CONFIDENCE INTERVALS FOR PREDICTIONS EXERCISES CHAPTER 15--OTHER REGRESSION MODELS 15.1 MULTIPLE LINEAR REGRESSION MODELS 15.1.1 Analysis of Variance 15.1.2 Problem of multicollinearity 15.2 REGRESSION WITH CATEGORICAL PREDICTORS 15.3 CURVILINEAR REGRESSION 15.4 TRANSFORMATIONS 15.5 OUTLIERS 15.6 COMMON MISTAKES IN REGRESSION EXERCISES FURTHER READING FOR PART III PART IV-EXPERIMENTAL DESIGN AND ANALYSIS CHAPTER 16-INTRODUCTION TO EXPERIMENTAL DESIGN 16.1 TERMINOLOGY 16.2 COMMON MISTAKES IN EXPERIMENTATION 16.3 TYPES OF EXPERIMENTAL DESIGNS 16-3.1 Simple designs 16.3.2 Full Factorial Design 16.3. 3 Fractional Factorial Designs EXERCISE CHAPTER 17-2 FACTORIAL DESIGNS 17.1 22 FACTORIAL DESIGNS 17.2 COMPUTATION OF EFFECTS 17.3 SIGN TABLE METHOD FOR CALCULATING EFFECTS 17.4 ALLOCATION OF VARIATION 17.5 GENERAL 2k FACTORIAL DESIGNS EXERCISE ChaPTER 18-2kr FACTORIAL DESIGNS WITH REPLICATIONS 18.1 22r FACTORIAL DESIGNS 18.2 COMPUTATION OF EFFECTS 18.3 ESTIMATION OF EXPERIMENTAL ERRORS 18.4 ALLOCATION OF VARIATION 18.5 CONFIDENCE INTERVALS FOR EFFECTS 18.6 CONFIDENCE INTERVALS FOR PREDICTED RESPONSES 18.7 VISUAL TESTS FOR VERIFYING THE ASSUMPTIONS 18.9 GENERAL 2kr FACTORIAL DESIGN EXERCISE CHAPTER 19--2k-P FRACTIONAL FACTORIAL DESIGNS 19.1 PREPARING THE SIGN TABLE FOR A k-n DESign 19.2 CONFOUNDING 19.3 ALGEBRA OF CONFOUNDING 19.4 DESIGN RESOLUTION EXERCISES CHAPTER 20--ONE-FACTOR EXPERIMENTS 20.1 MODEL 20.2 COMPUTATION OF EFFECTS 20.3 ESTIMATING EXPERIMENTAL ERRORS 20.4 ALLOCATION OF VARIATION 20. ANALYSIS OF VARIANCE 20.6 VISUAL DIAGNOSTIC TESTS 20.7 CONFIDENCE INTERVALS FOR EFFECTS 20.8 UNEQUAL SAMPLE SIZES EXERCISE CHAPTER 2I-TWO-FACTOR FULL FACTORIAL DESIGN WITHOUT REPLICATIONS 21I MODEL 21.2 COMPUTATION OF EFFECTS 21.3 ESTIMATING EXPERIMENTAL ERRORS 21.4 ALLOCATION OF VARIATION 21.5ANALYSIS OF VARIANCE 21.6 CONFIDENCE INTERVALS FOR EFFECTS 21.7 MULTIPLICATIVE MODELS FOR TWO-FACTOR EXPERIMENTS 21. 8 MISSING OBSERVATIONS EXERCISES CHAPTER 22-TWO-FACTOR FULL FACTORIAL DESIGN WITH REPLICATIONS 22.1 MODEL 22.2 COMPUTATION OF EFFECTS 22.3 COMPUTATION OF ERRORS 22.4 ALLOCATION OF VARIATION 22. ANALYSIS OF VARIANCE 22.6 CONFIDENCE INTERVALS FOR EFFECTS CHAPTER 23--GENERAL FULL FACTORIAL DESIGNS WITH K FACTORS 23.1 MODEL 23.2 ANALYSIS OF A GENERAL DESIGN 23.3 INFORMAL METHODS 23.3. 1 Observation method 23.3.2 Ran king Method 233.3 Range method EXERCISES FURTHER READING FOR PART IV PARTⅴ SIMuLaTion CHAPTER 24-INTRODUCTION TO SIMULATION 24.1 COMMON MISTAKES IN SIMULATION 24.2 OTHER CAUSES OF SIMULATION ANALYSIS FAILURE 24.3 TERMINOLOGY 24. 4 SELECTING A LANGUAGE FOR SIMULATION 24.5 TYPES OF SIMULATIONS 24.5.1 Monte Carlo simulation 24.5.2 Trace-Driven Simulation 24.5.3 Discrete-Event Simulations 24.6 EVENT-SET ALGORITHMS EXERCISES CHAPTER 25-ANALYSIS OF SIMULATION RESULTS 25.1 MODEL VERIFICATION TECHNIQUES 25.1.1 Top-Down Modular design 251.2 Antibugging 25.1.3 Structured walk-Through 25.1.4 Deterministic Models 25.1.5 Run Simplified cases 25.1.6 Trace 25.1.7On-Line Graphic displays 25.1.8 Continuity Test 25.1.9 Degeneracy Tests 25.1.10 Consistency Tests 25.1.11 Seed Independence 25.2 MODEL VALIDATION TECHNIQUES 25.2.1 Expert Intuition 25.2.2 Real-System Measurements 25.2.3 Theoretical results 25.3 TRANSIENT REMOVAL 25.3.1 Long ru 25.3.2 Proper Initialization 253.3 Truncation 25.3. 4 Initial Date Deletion 25.3.5 Moving Average of Independent Replications 25.3. 6 Batch means 25.4 TERMINATING SIMULATIONS 25.5 STOPPING CRITERIA: VARIANCE ESTIMATION 25.5.1 Independent replications 25.52 Batch means 25.5.3 Method of regeneration 25.6 VARIANCE REDUCTION EXERCISES CHAPTER 26-RANDOM-NUMBER GENERATION 26.1 DESIRED PROPERTIES OF A GOOD GENERATOR 26.2 LINEAR-CONGRUENTIAL GENERATORS 26.2.1 Multiplicative LCG 26.2.2 Multiplicative LCG with m= 2k 26.2.3 Multiplicative L CG with m 2K 26.3 TAUSWORTHE GENERATORS 26.4 EXTENDED FIBONACCI GENERATORS 26.5 COMBINED GENERATORS 266A SURVEY OF RANDOM-NUMBER GENERATORS 26.7 SEED SELECTION 26.8 MYTHS ABOUT RANDOMNUMBER GENERATION EXERCISES CHAPTER 27-TESTING RANDOM-NUMBER GENERATORS 27.1 CHI-SQUARE TEST 27.2 KOLMOGOROV-SMIRNOV TEST 27.3 SERIAL-CORRELATION TEST 27.4 TWO-LEVEL TESTS 27.5 K-DIMENSIONAL UNIFORMITY OR K-DISTRIBUTIVITY 27.6 SERIAL TEST 27.7 SPECTRAL TEST EXERCISES CHAPTER 28-RANDOM-VARIATE GENERATION 28.1 INVERSE TRANSFORMATION 28.2 REJECTION 28.3 COMPOSITION 28.4 CONVOLUTION 28.5 CHARACTERIZATION EXERCISE CHAPTER 29-COMMONLY USED DISTRIBUTIONS 29.1 BERNOULLI DISTRIBUTION 29.2 BETA DISTRIBUTION 29.3 BINOMIAL DISTRIBUTION 29. 4 CHI-SOUARE DISTRIBUTION 29.5 ERLANG DISTRIBUTION 29.6 EXPONENTIAL DISTRIBUTION 29.7F DISTRIBUTION 29.8 GAMMA DISTRIBUTION 29.9 GEOMETRIC DISTRIBUTION 29.10 LOGNORMAL DISTRIBUTION 29.1I NEGATIVE BINOMIIAL DISTRIBUTION 29.12 NORMAL DISTRIBUTION 29.13 PARETO DISTRIBUTION 29.14 PASCAL DISTRIBUTION 29.15 POISSON DISTRIBUTION 29.16 STUDENTS t DISTRIBUTION 29.17 UNIFORM DISTRIBUTION (CONTINUOUS) 29. 18 UNIFORM DISTRIBUTION DISCRETE) 29.19 WEIBULL DISTRIBUTION 29.20 RELATIONSHIIPS AMONG DISTRIBUTIONS EXERCISES FURTHER READING FOR PART V CURRENT AREAS OF RESEARCH IN SIMULATION PART VIQUEUEING MODELS CHAPTER 30--INTRODUCTION TO QUEUEING THEORY 30.1 QUEUEING NOTATION 30.2 RULES FOR ALL QUEUES 30.3 LITTLES LAW 30.4 TYPES OF STOCHASTIC PROCESSES EXERCISES CHAPTER 31-ANALYSIS OF A SINGLE QUEUE 31.1 BIRTH-DEATH PROCESSES 31.2 M/M/I QUEUE 31.3 M/M/m QUEUE 31. 4 MMMB OUEUE WITH FINITE BUFFERS 31.5 RESULTS FOR OTHER QUEUEING SYSTEMS EXERCISES CHAPTER 32-QUEUEING NETWORKS 32.1 OPEN AND CLOSED QUEUEING NETWORKS 32.2 PRODUCT FORM NETWORKS 32.3 QUEUEING NETWORK MODELS OF COMPUTER SYSTEMS EXERCISE CHAPTER 33--OPERATIONAL LAWS 33.1 UTILIZATION LAW 33. 2 FORCED FLOW LAW 33.3 LITTLE S LAW 33.4 GENERAL RESPONSE TIME LAW 33.5 INTERACTIVE RESPONSE TIME LAW 33.6 BOTTLENECK ANALYSIS EXERCISES CHAPTER 34--MEAN-VALUE ANALYSIS AND RELATED TECHNIQUES 34.1 ANALYSIS OF OPEN QUEUEING NETWORKS 34.2 MEAN-VALUE ANALYSIS 34.3 APPROXIMATE MVA 34. 4 BALANCED JOB BOUNDS EXERCISES CHAPTER 35--CONVOLUTION ALGORITHM 35.1 DISTRIBUTION OF JOBS INA SYSTEM 35.2 CONVOLUTION ALGORITHM FOR COMPUTING G(N 35.3 COMPUTING PERFORMANCE USING GN 35.4 TIMESHARING SYSTEMS EXERCISES CHAPTER 36-HERARCHIICAL DECOMPOSITION OF LARGE QUEUEING NETWORKS 36.1 LOAD-DEPENDENT SERVICE CENTERS 36.2 HIERARCHICAL DECOMPOSITION 36.3 LIMITATIONS OF QUEUEING THEORY EXERCISES FURTHER READING FOR PART VI SYMBOLS FREQUENTLY USED IN QUEUEING ANALYSIS References Appendix A 【实例截图】
【核心代码】
Amazon上的5星级图书，对优化系统非常有帮助
6.8.7 Clustering Techniques 6.8.8 Minimum Spanning Tree Method 6.8.9 Cluster Interpretation 6.8.10 Problems with clustering EXERCISES CHAPTER7— MONITORS 7.1 MONITOR TERMINOLOGY 7.2 MONITOR CLASSIFICATION 7. 3 SOFTWARE MONITORS 7.3.1 Issues In Software Monitor Design 7. 4 HARDWARE MONITORS 7.5 SOFTWARE VERSUS HARDWARE MONITORS 7.6 FIRMWARE AND HYBRID MONITORS 7.7 DISTRIBUTED-SYSTEM MONITORS 77。1 Observation 7.7.2 Collection 7.7.3 Analysis 7.7.4 Presentation 7.7.5 Interpretation 7.7.6 Console Functions EXERCISES chAPTER8-PROGRAM EXECUTION MONITORS AND ACCOUNTING LOGS 8.1 PROGRAM EXECUTION MONITORS 8.1.1 Issues In designing a program execution monitor 8.2 TECHNIQUES FOR IMPROVING PROGRAM PERFORMANCE 8. 3 ACCOUNTING LOGS 8. 4 ANALYSIS AND INTERPRETATION OF ACCOUNTING LOG DATA 8.5 USING ACCOUNTING LOGS TO ANSWER COMMONLY ASKED QUESTIONS EXERCISE ChAPTER 9-CAPACITY PLANNING AND BENCHMARKING 9.1 STEPS IN CAPACITY PLANNING AND MANAGEMENT 9.2 PROBLEMS IN CAPACITY PLANNING 9. 3 COMMON MISTAKES IN BENCHMARKING 9. 4 BENCHMARKING GAMES 9.5 LOAD DRIVERS 9.6 REMOTE-TERMIINAL EMULATION 9.7 COMPONENTS OF AN RTE 9. 8 LIMITATIONS OF CURRENT RTES EXERCISES CHAPTER 10-THE ART OF DATA PRESENTATION 10.1 TYPES OF VARIABLES 10.2 GUIDELINES FOR PREPARING GOOD GRAPHIC CHARTS 10.3 COMMON MISTAKES IN PREPARING CHARTS 10.4 PICTORIAL GAMES 10.5 GANTT CHARTS 10.6 KIVIAT GRAPHS 10.6.1 Shapes of Kiviat Graphs 10.6.2 Application of Kiviat Graphs to Other Systems 10.7 SCHUMACHER CHARTS 10. 8 DECISION MAKERS GAMES CHAPTER 11-RATIO GAMES 11.1 CHOOSING AN APPROPRIATE BASE SYSTEM 11.2 USING AN APPROPRIATE RATIO METRIC 11.3 USING RELATIVE PERFORMANCE ENHANCEMENT 11. 4 RATIO GAMES WITH PERCENTAGES 11.5 STRATEGIES FOR WINNING A RATIO GAME 11.6 CORRECT ANALYSIS EXERCISES FURTHER READING FOR PART II PART IIL-PROBABILITY THEORY AND STATISTICS CHAPTER 12-SUMMARIZING MEASURED DATA 12.1 BASIC PROBABILITY AND STATISTICS CONCEPTS 12.2 SUMMARIZING DATA BY A SINGLE NUMBER 2.3 SELECTING AMONG THE MEAN MEDIAN AND MODE 12.4 COMMON MISUSES OF MEANS 12.5 GEOMETRIC MEAN 12.6 HARMONIC MEAN 12.7 MEAN OF A RATIO 12. 8 SUMMARIZING VARIABILITY 12.9 SELECTING THE INDEX OF DISPERSION 12.10 DETERMINING DISTRIBUTION OF DATA EXERCISES CHAPTER 13-COMPARING SYSTEMS USING SAMPLE DATA 13.1 SAMPLE VERSUS POPULATION 13.2 CONFIDENCE INTERVAL FOR THE MEAN 13.3 TESTING FOR A ZERO MEAN 13. 4 COMPARING TWO ALTERNATIVES 13. 4.1 Paired observations 13.4.2 Unpaired observations 13.4.3 Approximate visual tes 13.5 WHAT CONFIDENCE LEVEL TO USE 13.6 HYPOTHESIS TESTING VERSUS CONFIDENCE INTERVALS 13.7 ONE-SIDED CONFIDENCE INTERVALS 13. 8 CONFIDENCE INTERVALS FOR PROPORTIONS 13.9 DETERMINING SAMPLE SIZE 13.9.1 Sample Size for Determining Mean 13.9.2 Sample size for Determining proportions 13.9.3 Sample Size for Comparing Two Alternatives CHAPTER 14SIMPLE LINEAR REGRESSION MODELS 14.1 DEFINITION OF A GOOD MODEL 14.2 ESTIMATION OF MODEL PARAMETERS 14.5 CONFIDENCE INTERVALS FOR REGRESSION PARAMETERS 14.6 CONFIDENCE INTERVALS FOR PREDICTIONS EXERCISES CHAPTER 15--OTHER REGRESSION MODELS 15.1 MULTIPLE LINEAR REGRESSION MODELS 15.1.1 Analysis of Variance 15.1.2 Problem of multicollinearity 15.2 REGRESSION WITH CATEGORICAL PREDICTORS 15.3 CURVILINEAR REGRESSION 15.4 TRANSFORMATIONS 15.5 OUTLIERS 15.6 COMMON MISTAKES IN REGRESSION EXERCISES FURTHER READING FOR PART III PART IV-EXPERIMENTAL DESIGN AND ANALYSIS CHAPTER 16-INTRODUCTION TO EXPERIMENTAL DESIGN 16.1 TERMINOLOGY 16.2 COMMON MISTAKES IN EXPERIMENTATION 16.3 TYPES OF EXPERIMENTAL DESIGNS 16-3.1 Simple designs 16.3.2 Full Factorial Design 16.3. 3 Fractional Factorial Designs EXERCISE CHAPTER 17-2 FACTORIAL DESIGNS 17.1 22 FACTORIAL DESIGNS 17.2 COMPUTATION OF EFFECTS 17.3 SIGN TABLE METHOD FOR CALCULATING EFFECTS 17.4 ALLOCATION OF VARIATION 17.5 GENERAL 2k FACTORIAL DESIGNS EXERCISE ChaPTER 18-2kr FACTORIAL DESIGNS WITH REPLICATIONS 18.1 22r FACTORIAL DESIGNS 18.2 COMPUTATION OF EFFECTS 18.3 ESTIMATION OF EXPERIMENTAL ERRORS 18.4 ALLOCATION OF VARIATION 18.5 CONFIDENCE INTERVALS FOR EFFECTS 18.6 CONFIDENCE INTERVALS FOR PREDICTED RESPONSES 18.7 VISUAL TESTS FOR VERIFYING THE ASSUMPTIONS 18.9 GENERAL 2kr FACTORIAL DESIGN EXERCISE CHAPTER 19--2k-P FRACTIONAL FACTORIAL DESIGNS 19.1 PREPARING THE SIGN TABLE FOR A k-n DESign 19.2 CONFOUNDING 19.3 ALGEBRA OF CONFOUNDING 19.4 DESIGN RESOLUTION EXERCISES CHAPTER 20--ONE-FACTOR EXPERIMENTS 20.1 MODEL 20.2 COMPUTATION OF EFFECTS 20.3 ESTIMATING EXPERIMENTAL ERRORS 20.4 ALLOCATION OF VARIATION 20. ANALYSIS OF VARIANCE 20.6 VISUAL DIAGNOSTIC TESTS 20.7 CONFIDENCE INTERVALS FOR EFFECTS 20.8 UNEQUAL SAMPLE SIZES EXERCISE CHAPTER 2I-TWO-FACTOR FULL FACTORIAL DESIGN WITHOUT REPLICATIONS 21I MODEL 21.2 COMPUTATION OF EFFECTS 21.3 ESTIMATING EXPERIMENTAL ERRORS 21.4 ALLOCATION OF VARIATION 21.5ANALYSIS OF VARIANCE 21.6 CONFIDENCE INTERVALS FOR EFFECTS 21.7 MULTIPLICATIVE MODELS FOR TWO-FACTOR EXPERIMENTS 21. 8 MISSING OBSERVATIONS EXERCISES CHAPTER 22-TWO-FACTOR FULL FACTORIAL DESIGN WITH REPLICATIONS 22.1 MODEL 22.2 COMPUTATION OF EFFECTS 22.3 COMPUTATION OF ERRORS 22.4 ALLOCATION OF VARIATION 22. ANALYSIS OF VARIANCE 22.6 CONFIDENCE INTERVALS FOR EFFECTS CHAPTER 23--GENERAL FULL FACTORIAL DESIGNS WITH K FACTORS 23.1 MODEL 23.2 ANALYSIS OF A GENERAL DESIGN 23.3 INFORMAL METHODS 23.3. 1 Observation method 23.3.2 Ran king Method 233.3 Range method EXERCISES FURTHER READING FOR PART IV PARTⅴ SIMuLaTion CHAPTER 24-INTRODUCTION TO SIMULATION 24.1 COMMON MISTAKES IN SIMULATION 24.2 OTHER CAUSES OF SIMULATION ANALYSIS FAILURE 24.3 TERMINOLOGY 24. 4 SELECTING A LANGUAGE FOR SIMULATION 24.5 TYPES OF SIMULATIONS 24.5.1 Monte Carlo simulation 24.5.2 Trace-Driven Simulation 24.5.3 Discrete-Event Simulations 24.6 EVENT-SET ALGORITHMS EXERCISES CHAPTER 25-ANALYSIS OF SIMULATION RESULTS 25.1 MODEL VERIFICATION TECHNIQUES 25.1.1 Top-Down Modular design 251.2 Antibugging 25.1.3 Structured walk-Through 25.1.4 Deterministic Models 25.1.5 Run Simplified cases 25.1.6 Trace 25.1.7On-Line Graphic displays 25.1.8 Continuity Test 25.1.9 Degeneracy Tests 25.1.10 Consistency Tests 25.1.11 Seed Independence 25.2 MODEL VALIDATION TECHNIQUES 25.2.1 Expert Intuition 25.2.2 Real-System Measurements 25.2.3 Theoretical results 25.3 TRANSIENT REMOVAL 25.3.1 Long ru 25.3.2 Proper Initialization 253.3 Truncation 25.3. 4 Initial Date Deletion 25.3.5 Moving Average of Independent Replications 25.3. 6 Batch means 25.4 TERMINATING SIMULATIONS 25.5 STOPPING CRITERIA: VARIANCE ESTIMATION 25.5.1 Independent replications 25.52 Batch means 25.5.3 Method of regeneration 25.6 VARIANCE REDUCTION EXERCISES CHAPTER 26-RANDOM-NUMBER GENERATION 26.1 DESIRED PROPERTIES OF A GOOD GENERATOR 26.2 LINEAR-CONGRUENTIAL GENERATORS 26.2.1 Multiplicative LCG 26.2.2 Multiplicative LCG with m= 2k 26.2.3 Multiplicative L CG with m 2K 26.3 TAUSWORTHE GENERATORS 26.4 EXTENDED FIBONACCI GENERATORS 26.5 COMBINED GENERATORS 266A SURVEY OF RANDOM-NUMBER GENERATORS 26.7 SEED SELECTION 26.8 MYTHS ABOUT RANDOMNUMBER GENERATION EXERCISES CHAPTER 27-TESTING RANDOM-NUMBER GENERATORS 27.1 CHI-SQUARE TEST 27.2 KOLMOGOROV-SMIRNOV TEST 27.3 SERIAL-CORRELATION TEST 27.4 TWO-LEVEL TESTS 27.5 K-DIMENSIONAL UNIFORMITY OR K-DISTRIBUTIVITY 27.6 SERIAL TEST 27.7 SPECTRAL TEST EXERCISES CHAPTER 28-RANDOM-VARIATE GENERATION 28.1 INVERSE TRANSFORMATION 28.2 REJECTION 28.3 COMPOSITION 28.4 CONVOLUTION 28.5 CHARACTERIZATION EXERCISE CHAPTER 29-COMMONLY USED DISTRIBUTIONS 29.1 BERNOULLI DISTRIBUTION 29.2 BETA DISTRIBUTION 29.3 BINOMIAL DISTRIBUTION 29. 4 CHI-SOUARE DISTRIBUTION 29.5 ERLANG DISTRIBUTION 29.6 EXPONENTIAL DISTRIBUTION 29.7F DISTRIBUTION 29.8 GAMMA DISTRIBUTION 29.9 GEOMETRIC DISTRIBUTION 29.10 LOGNORMAL DISTRIBUTION 29.1I NEGATIVE BINOMIIAL DISTRIBUTION 29.12 NORMAL DISTRIBUTION 29.13 PARETO DISTRIBUTION 29.14 PASCAL DISTRIBUTION 29.15 POISSON DISTRIBUTION 29.16 STUDENTS t DISTRIBUTION 29.17 UNIFORM DISTRIBUTION (CONTINUOUS) 29. 18 UNIFORM DISTRIBUTION DISCRETE) 29.19 WEIBULL DISTRIBUTION 29.20 RELATIONSHIIPS AMONG DISTRIBUTIONS EXERCISES FURTHER READING FOR PART V CURRENT AREAS OF RESEARCH IN SIMULATION PART VIQUEUEING MODELS CHAPTER 30--INTRODUCTION TO QUEUEING THEORY 30.1 QUEUEING NOTATION 30.2 RULES FOR ALL QUEUES 30.3 LITTLES LAW 30.4 TYPES OF STOCHASTIC PROCESSES EXERCISES CHAPTER 31-ANALYSIS OF A SINGLE QUEUE 31.1 BIRTH-DEATH PROCESSES 31.2 M/M/I QUEUE 31.3 M/M/m QUEUE 31. 4 MMMB OUEUE WITH FINITE BUFFERS 31.5 RESULTS FOR OTHER QUEUEING SYSTEMS EXERCISES CHAPTER 32-QUEUEING NETWORKS 32.1 OPEN AND CLOSED QUEUEING NETWORKS 32.2 PRODUCT FORM NETWORKS 32.3 QUEUEING NETWORK MODELS OF COMPUTER SYSTEMS EXERCISE CHAPTER 33--OPERATIONAL LAWS 33.1 UTILIZATION LAW 33. 2 FORCED FLOW LAW 33.3 LITTLE S LAW 33.4 GENERAL RESPONSE TIME LAW 33.5 INTERACTIVE RESPONSE TIME LAW 33.6 BOTTLENECK ANALYSIS EXERCISES CHAPTER 34--MEAN-VALUE ANALYSIS AND RELATED TECHNIQUES 34.1 ANALYSIS OF OPEN QUEUEING NETWORKS 34.2 MEAN-VALUE ANALYSIS 34.3 APPROXIMATE MVA 34. 4 BALANCED JOB BOUNDS EXERCISES CHAPTER 35--CONVOLUTION ALGORITHM 35.1 DISTRIBUTION OF JOBS INA SYSTEM 35.2 CONVOLUTION ALGORITHM FOR COMPUTING G(N 35.3 COMPUTING PERFORMANCE USING GN 35.4 TIMESHARING SYSTEMS EXERCISES CHAPTER 36-HERARCHIICAL DECOMPOSITION OF LARGE QUEUEING NETWORKS 36.1 LOAD-DEPENDENT SERVICE CENTERS 36.2 HIERARCHICAL DECOMPOSITION 36.3 LIMITATIONS OF QUEUEING THEORY EXERCISES FURTHER READING FOR PART VI SYMBOLS FREQUENTLY USED IN QUEUEING ANALYSIS References Appendix A 【实例截图】
【核心代码】
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