Art of Computer System Performance Analysis Techniques

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Art of Computer Systems Performance Analysis Techniques For Experimental Design
Measurements Simulation And Modeling
by Raj Jain
Wiley Computer Publishing, John Wiley & Sons, Inc.
ISBN: 0471503363   Pub Date: 05/01/91
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Preface
Acknowledgments
PART I—AN OVERVIEW OF PERFORMANCE EVALUATION
CHAPTER 1—INTRODUCTION
1.1 OUTLINE OF TOPICS
1.2 THE ART OF PERFORMANCE EVALUATION
1.3 PROFESSIONAL ORGANIZATIONS, JOURNALS, AND CONFERENCES
1.4 PERFORMANCE PROJECTS
EXERCISE
CHAPTER 2—COMMON MISTAKES AND HOW TO AVOID
THEM
2.1 COMMON MISTAKES IN PERFORMANCE EVALUATION
2.2 A SYSTEMATIC APPROACH TO PERFORMANCE EVALUATION
EXERCISES
CHAPTER 3—SELECTION OF TECHNIQUES AND METRICS
3.1 SELECTING AN EVALUATION TECHNIQUE
3.2 SELECTING PERFORMANCE METRICS
3.3 COMMONLY USED PERFORMANCE METRICS
3.4 UTILITY CLASSIFICATION OF PERFORMANCE METRICS
3.5 SETTING PERFORMANCE REQUIREMENTS
EXERCISES
Title
-----------

FURTHER READING FOR PART I
PART II—MEASUREMENT TECHNIQUES AND TOOLS
CHAPTER 4—TYPES OF WORKLOADS
1 ADDITION INSTRUCTION
2 INSTRUCTION MIXES
3 KERNELS
4 SYNTHETIC PROGRAMS
5 APPLICATION BENCHMARKS
6 POPULAR BENCHMARKS
6.1 Sieve
6.2 Ackermann’s Function
6.3 Whetstone
6.4 LINPACK
6.5 Dhrystone
6.6 Lawrence Livermore Loops
6.7 Debit-Credit Benchmark
6.8 SPEC Benchmark Suite
EXERCISES
CHAPTER 5—THE ART OF WORKLOAD SELECTION
1 SERVICES EXERCISED
2 LEVEL OF DETAIL
3 REPRESENTATIVENESS
4 TIMELINESS
5 OTHER CONSIDERATIONS IN WORKLOAD SELECTION
EXERCISES
CHAPTER 6—WORKLOAD CHARACTERIZATION
TECHNIQUES
1 TERMINOLOGY
2 AVERAGING
3 SPECIFYING DISPERSION
4 SINGLE-PARAMETER HISTOGRAMS
5 MULTIPARAMETER HISTOGRAMS
6 PRINCIPAL-COMPONENT ANALYSIS
7 MARKOV MODELS
8 CLUSTERING
8.1 Sampling
8.2 Parameter Selection
8.3 Transformation
8.4 Outliers
8.5 Data Scaling
8.6 Distance Metric

8.7 Clustering Techniques
8.8 Minimum Spanning Tree Method
8.9 Cluster Interpretation
8.10 Problems with Clustering
EXERCISES
CHAPTER 7—MONITORS
1 MONITOR TERMINOLOGY
2 MONITOR CLASSIFICATION
3 SOFTWARE MONITORS
3.1 Issues In Software Monitor Design
4 HARDWARE MONITORS
5 SOFTWARE VERSUS HARDWARE MONITORS
6 FIRMWARE AND HYBRID MONITORS
7 DISTRIBUTED-SYSTEM MONITORS
7.1 Observation
7.2 Collection
7.3 Analysis
7.4 Presentation
7.5 Interpretation
7.6 Console Functions
EXERCISES
CHAPTER 8—PROGRAM EXECUTION MONITORS AND
ACCOUNTING LOGS
1 PROGRAM EXECUTION MONITORS
1.1 Issues In Designing a Program Execution Monitor
2 TECHNIQUES FOR IMPROVING PROGRAM PERFORMANCE
3 ACCOUNTING LOGS
4 ANALYSIS AND INTERPRETATION OF ACCOUNTING LOG DATA
5 USING ACCOUNTING LOGS TO ANSWER COMMONLY ASKED
QUESTIONS
EXERCISE
CHAPTER 9—CAPACITY PLANNING AND BENCHMARKING
1 STEPS IN CAPACITY PLANNING AND MANAGEMENT
2 PROBLEMS IN CAPACITY PLANNING
3 COMMON MISTAKES IN BENCHMARKING
4 BENCHMARKING GAMES
5 LOAD DRIVERS
6 REMOTE-TERMINAL EMULATION
7 COMPONENTS OF AN RTE
8 LIMITATIONS OF CURRENT RTES
EXERCISES
CHAPTER 10—THE ART OF DATA PRESENTATION

1 TYPES OF VARIABLES
2 GUIDELINES FOR PREPARING GOOD GRAPHIC CHARTS
3 COMMON MISTAKES IN PREPARING CHARTS
4 PICTORIAL GAMES
5 GANTT CHARTS
6 KIVIAT GRAPHS
6.1 Shapes of Kiviat Graphs
6.2 Application of Kiviat Graphs to Other Systems
7 SCHUMACHER CHARTS
8 DECISION MAKER’S GAMES
CHAPTER 11—RATIO GAMES
1 CHOOSING AN APPROPRIATE BASE SYSTEM
2 USING AN APPROPRIATE RATIO METRIC
3 USING RELATIVE PERFORMANCE ENHANCEMENT
4 RATIO GAMES WITH PERCENTAGES
5 STRATEGIES FOR WINNING A RATIO GAME
6 CORRECT ANALYSIS
EXERCISES
FURTHER READING FOR PART II
PART III—PROBABILITY THEORY AND STATISTICS
CHAPTER 12—SUMMARIZING MEASURED DATA
1 BASIC PROBABILITY AND STATISTICS CONCEPTS
2 SUMMARIZING DATA BY A SINGLE NUMBER
3 SELECTING AMONG THE MEAN, MEDIAN, AND MODE
4 COMMON MISUSES OF MEANS
5 GEOMETRIC MEAN
6 HARMONIC MEAN
7 MEAN OF A RATIO
8 SUMMARIZING VARIABILITY
9 SELECTING THE INDEX OF DISPERSION
10 DETERMINING DISTRIBUTION OF DATA
EXERCISES
CHAPTER 13—COMPARING SYSTEMS USING SAMPLE DATA
1 SAMPLE VERSUS POPULATION
2 CONFIDENCE INTERVAL FOR THE MEAN
3 TESTING FOR A ZERO MEAN
4 COMPARING TWO ALTERNATIVES
4.1 Paired Observations
4.2 Unpaired Observations
4.3 Approximate Visual Test

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 14—SIMPLE 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
k
 FACTORIAL DESIGNS
17.1 2
2
 FACTORIAL DESIGNS
17.2 COMPUTATION OF EFFECTS
17.3 SIGN TABLE METHOD FOR CALCULATING EFFECTS
17.4 ALLOCATION OF VARIATION
17.5 GENERAL 2
k
 FACTORIAL DESIGNS