Henry H. Liu, PhD, is Software Developer at BMC Software. Previously, he worked as a physicist in the national labs of China, France, Germany, and the United States. He also worked at Intel and Amdocs as a software performance engineer prior to joining BMC. He was an Alexander von Humboldt Research Fellow from 1990–1992 in Germany. He was awarded the Best Paper Award at CMG's 2004 conference in the category of software performance engineering. He is a certified Sun Enterprise Architect, IBM XML Developer, and Microsoft .NET Developer. Dr. Liu is most interested in applying his previous scientific research disciplines to solving software performance and scalability challenges.

• PREFACE xv ACKNOWLEDGMENTS xxiIntroduction 1Performance versus Scalability 1PART 1 THE BASICS 31. Hardware Platform 51.1 Turing Machine 61.2 von Neumann Machine 71.3 Zuse Machine 81.4 Intel Machine 91.5 Sun Machine 171.6 System Under Test 181.7 Odds Against Turing 301.8 Sizing Hardware 351.9 Summary 372. Software Platform 412.1 Software Stack 422.2 APIs 442.3 Multithreading 472.4 Categorizing Software 5352.5 Enterprise Computing 552.6 Summary 633. Testing Software Performance and Scalability 653.1 Scope of Software Performance and Scalability Testing 673.2 Software Development Process 833.3 Defining Software Performance 863.4 Stochastic Nature of Software Performance Measurements 953.5 Amdahl’s Law 973.6 Software Performance and Scalability Factors 993.7 System Performance Counters 1113.8 Software Performance Data Principles 1293.9 Summary 131PART 2 APPLYING QUEUING THEORY 1354. Introduction to Queuing Theory 1374.1 Queuing Concepts and Metrics 1394.2 Introduction to Probability Theory 1434.3 Applying Probability Theory to Queuing Systems 1454.4 Queuing Models for Networked Queuing Systems 1534.5 Summary 1725. Case Study I: Queuing Theory Applied to SOA 1775.1 Introduction to SOA 1785.2 XML Web Services 1795.3 The Analytical Model 1815.4 Service Demand 1835.5 MedRec Application 1885.6 MedRec Deployment and Test Scenario 1895.7 Test Results 1915.8 Comparing the Model with the Measurements 1985.9 Validity of the SOA Performance Model 2005.10 Summary 2006. Case Study II: Queuing Theory Applied to Optimizing and Tuning Software Performance and Scalability 2056.1 Analyzing Software Performance and Scalability 2076.2 Effective Optimization and Tuning Techniques 2206.3 Balanced Queuing System 2406.4 Summary 244PART 3 APPLYING API PROFILING 2497. Defining API Profiling Framework 2517.1 Defense Lines Against Software Performance and Scalability Defects 2527.2 Software Program Execution Stack 2537.3 The PerfBasic API Profiling Framework 2547.4 Summary 2608. Enabling API Profiling Framework 2638.1 Overall Structure 2648.2 Global Parameters 2658.3 Main Logic 2668.4 Processing Files 2668.5 Enabling Profiling 2678.6 Processing Inner Classes 2708.7 Processing Comments 2718.8 Processing Method Begin 2728.9 Processing Return Statements 2748.10 Processing Method End 2758.11 Processing Main Method 2768.12 Test Program 2778.13 Summary 2799. Implementing API Profiling Framework 2819.1 Graphics Tool—dot 2819.2 Graphics Tool—ILOG 2849.3 Graphics Resolution 2869.4 Implementation 2879.5 Summary 30010. Case Study: Applying API Profiling to Solving Software Performance and Scalability Challenges 30310.1 Enabling API Profiling 30410.2 API Profiling with Standard Logs 31310.3 API Profiling with Custom Logs 32010.4 API Profiling with Combo Logs 32510.5 Applying API Profiling to Solving Performance and Scalability Problems 33310.6 Summary 337APPENDIX A STOCHASTIC EQUILIBRIUM AND ERGODICITY 339A.1 Basic Concepts 339A.2 Classification of Random Processes 343A.3 Discrete-Time Markov Chains 345A.4 Continuous-Time Markov Chains 349A.5 Stochastic Equilibrium and Ergodicity 351A.6 Birth–Death Chains 357APPENDIX B MEMORYLESS PROPERTY OF THE EXPONENTIAL DISTRIBUTION 361APPENDIX C M/M/1 QUEUES AT STEADY STATE 363C.1 Review of Birth–Death Chains 363C.2 Utilization and Throughput 364C.3 Average Queue Length in the System 365C.4 Average System Time 365C.5 Average Wait Time 366INDEX 367

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