Publisher: McGraw-Hill, 1992, 612 pages

ISBN: 0-07-033586-9

Keywords: Programming

**Part I: Basic Concepts****Performance Measures and Evaluation Techniques**- Evaluation Metrics
- Techniques of Performance Evaluation
- Applications of Performance Metrics
- Workload Characterization
- Benchmarking Computer Systems
- Exercises
**Measurement Techniques**- Classification of Measurement Techniques
- Hardware Monitoring
- Software Monitoring
- Hybrid Monitoring
- Other Issues in Measurement
- Exercises
**Experiment Design and Data Analysis**- Simulation Techniques
- Fundamentals of Data Analysis
- Organizing Simulation Runs
- Selection of Inputs
- Comparison of Alternate Designs
- Regression Analysis
- Variance Reduction Techniques
- Exercises
**Fundamentals of Queuing Models**- Structure and Performance Parameters
- Operational Analysis of Queuing Models
- General Features of Queuing Models
- Analysis of Multiple-Class Networks
- Calibration of Queuing Models
- Exercises
**Elementary Stochastic Analysis**- Random Processes
- Analysis of Markov Chains
- Long-Term Behavior of Markov Chains
- Birth and Death Processes
- Steady-State Analysis of M/M Systems
- Batch Systems and Methods of Stages
- Exercises
**Product-Form Queuing Network Models**- Characteristics of Product-Form Solutions
- Open Queuing Network Models
- Closed Product-Form Networks
- Multiple-Class Networks
- Algorithms for Closed PF Networks
- Conditions for Product-Form Solution
- Exercises
**Basic Algorithms for Product-Form Networks**- Single-Chain Convolution
- Single-Chain Mean Value Analysis
- Multiple-Chain Convolutions
- Multiple-Chain Mean Value Analysis
- The LBANC Algorithm
- Algorithms for Mixed Networks
- Approximate Mean Value Analysis
- Exercises
**Aggregation and Approximate Modeling**- Flow-Equivalent Aggregation
- Applications of Aggregation
- Modeling Non-PF Scheduling Disciplines
- Decomposition Approximations
- Exercises
**Part II: Advanced Topics****Advanced Stochastic Analysis**- Solution Using Generating Functions
- Some Results on General Queuing Systems
- The M/G/1 Queuing System
- Phase-Type Distributions
- Matrix-Analytic Methods
- Additional Topics
- Exercises
**Algorithms for Networks with Specialized Features**- Networks with Advanced Features
- Chain-Based Recursion Algorithms
- Algorithms Exploiting Sparsity
- Asymptotic Expansions
- Exercises
**Bounds on Performance**- Bounds on Single-Chain Networks
- Networks with Load-Dependent Stations
- Bounds on Mean Queue Lengths
- Bounds for Multiple-Chain Networks
- Exercises
**Petri Net-Based Performance Modeling**- Classical Petri Nets
- Timed Petri Nets
- Generalized Stochastic Petri Nets
- Discrete Time Petri Nets
- Modeling Multiprocessor Systems
- Extensions to Stochastic Petri Nets
- Product-Form Solutions
- Exercises
**Selected Applications**- Analysis of Polling Systems
- Problems in Performance Optimization
- Analysis of Jobs with Internal Concurrency
- Modeling Fault-Tolerant Systems
- Exercises
**Part III: Appendices****Notation****Introduction to probability Theory**- Basic Concepts
- Events and the Probability Measure
- Random Variables and Distributions
- Functions of a Random Variable
- Expectation and Moments
- Some Inequalities
- Summary of Selected Distributions
- Discrete Distributions
- Continuous Distributions
- Characteristic Functions
- The Z-Transform
- LaPlace Transform
- General Characteristic Function
- Exercises
**A Suggested Modeling Project**- Project Overview
- Operational Details of SYS-A
- Hardware Configuration
- System Operation
- Model Construction
- Model Description
- Modeling of Minor Overheads
- Model Calibration and Validation
- Modeling Enhancements
**Performance Tuning Using Solver**- Overview of Solver
- Tuning a Paging System
- The Solver Program
- Interpretation of Results
- Issues for Further Study
- Program Listing and Output
**Selected Tables**- Critical Points of
*t*Distribution - Selection among Alternate Designs
- Selected LaPlace Transform Pairs
- Selected z-Transform Pairs

If this is the introduction, I don't want to read an advanced book.

You need a Ph.D. in Computer Science and at least a M.Sc. in Mathematics to understand what he is talking about. It may be an excellent book, but I have no way of knowing. Someone call me and tell me…

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