четверг, 20 мая 2010 г.

E-book: Software Performance and Scalability - A Quantitative Approach

Эта книга получила 8 пятизвездочных отзывов(из 8) на Амазоне, что уже о многом говорит. Грамотный баланс теории и практических примеров, разбор реальных ситуаций "на пальцах", задания для усвоения материала. Для itшных гуру целый раздел о практическом применении теории массового обслуживания (теория очередей), а также примеры профайлинга API.

Download e-book Software Performance and Scalability - A Quantitative Approach




Introduction: Performance versus Scalability.
PART 1 THE BASICS.
1 Hardware Platform.
1.1 Turning Machine.
1.2 von Neumann Machine.
1.3 Zuse Machine.
1.4 Intel Machine.
1.5 Sun Machine.
1.6 System Under Test.
1.7 Odds Against Turing.
1.8 Sizing Hardware.
1.9 Summary.
Recommended Reading.
Exercises.
2 Software Platform.
2.1 Software Stack.
2.2 APIs.
2.3 Multithreading.
2.4 Categorizing Software.
2.5 Enterprise Computing.
2.6 Summary.
Recommended Reading.
Exercises.
3 Testing Software Performance and Scalability.
3.1 Scope of Software Performance and Scalability Testing.
3.2 Software Development Performance.
3.3 Defining Software Performance.
3.4 Stochastic Nature of Software Performance Measurements.
3.5 Amdahl’s Law.
3.6 Software Performance and Scalability Factors.
3.7 System Performance Counters.
3.8 Software Performance Data Principles.
3.9 Summary.
Recommended Reading.
Exercises.
PART 2 APPLYING QUEUING THEORY.
4 Introduction to Queuing Theory.
4.1 Queuing Concepts and Metrics.
4.2 Introduction to Probability Theory.
4.3 Applying Probability Theory to Queuing Systems.
4.4 Queuing Models for Networked Queuing Systems.
4.5 Summary.
Recommended Reading.
Exercises.
5 Case Study I: Queuing Theory to SOA.
5.1 Introduction to SOA.
5.2 XML Web Services.
5.3 The Analytical Model.
5.4 Service Demand.
5.5 MedRec Application.
5.6 MedRec Deployment and Test Scenario.
5.7 Test Results.
5.8 Comparing the Model with the Measurements.
5.9 Validity of the SOA Performance Model.
5.10 Summary.
Recommended Reading.
Exercises.
6 Caser Study II: Queuing Theory Applied to Optimizing and Tuning Software Performance and
Scalability.
6.1 Analyzing Software Performance and Scalability.
6.2 Effective Optimization and Tuning Techniques.
6.3 Balanced Queuing System.
6.4 Summary.
Recommended Reading.
Exercises.
PART 3 APPLYING API PROFILING.
7 Defining API Profiling Framework.
7.1 Defense Lines Against Software Performance and Scalability Defects.
7.2 Software Program Execution Stack.
7.3 The PerfBasic API Profiling Framework.
7.4 Summary.
Exercises.
8 Enabling API Profiling Framework.
8.1 Overall Structure.
8.2 Global Parameters.
8.3 Main Logic.
8.4 Processing Files.
8.5 Enabling Profiling.
8.6 Processing Inner Classes.
8.7 Processing Comments.
8.8 Processing Methods Begin.
8.9 Processing Return Statements.
8.10 Processing Method End.
8.11 Processing Main Method.
8.12 Test Program.
8.13 Summary.
Recommended Reading.
Exercises.
9. Implementing API Profiling Framework.
9.1 Graphics Tool—dot.
9.2 Graphics Tools—ILOG.
9.3 Graphics Resolution.
9.4 Implementation.
9.5 Summary.
Exercises.
10 Case Study: Applying API Profiting to Solving Software Performance and Scalability Challenges.
10.1 Enabling API Profiling.
10.2 API Profiling with Standard Logs.
10.3 API Profiling with Custom Logs.
10.4 API Profiling with Combo Logs.
10.5 Applying API Profiling to Solving Performance and Scalability Problems.
10.6 Summary.
Exercises.
APPENDIX A STOCHASTIC EQUILIBRIUM AND ERGODICITY.
A.1 Basic Concepts.
A.2 Classification of Random Processes.
A.3 Discrete-Time Markov Chains.
A.4 Continuous-Time Markov Chains.
A.5 Stochastic Equilibrium and Ergodicity.
A.6 Birth-Death Chains.
APPENDIX B MEMORYLESS PROPERTY OF THE EXPONENTIAL DISTRIBUTION.
APPENDIX C M/M/1 QUEUES AT STEADY STATE.
C.1 Review of Birth-Death Chains.
C.2 Utilization and Throughput.
C.3 Average Queue Length in the System.
C.4 Average System Time.
C.5 Average Wait Time.

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  1. Не рабочая уже ссылка на книгу, перезалейте , пожалуйста

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