Software Performance and Scalability A Quantitative Approach

by Liu, Henry H.

• ISBN13: 9780470462539
• ISBN10: 0470462531
• Edition: 1st
• Format: Hardcover
• Copyright: 2009-05-26
• Publisher: Wiley-Blackwell
• More Book Details
• Purchase Benefits

Summary

This is the first book to take a quantitative approach to the subject of software performance and scalability. It brings together three unique perspectives to demonstrate how your products can be optimized and tuned for the best possible performance and scalability:- The Basics-introduces the computer hardware and software architectures that predetermine the performance and scalability of a software product as well as the principles of measuring the performance and scalability of a software product- Queuing Theory-explains the performance laws and queuing models for interpreting the underlying physics behind software performance and scalability, supplemented with ready-to-apply techniques for improving the performance and scalability of a software system- API Profiling-shows you how to design more efficient algorithms and achieve optimized performance and scalability, aided by adopting an API profiling framework (perfBasic) built on the concept of a performance map for drilling down performance root causes at the API levelThis book gives a specialized skill set that enables you to design and build performance into your products with immediate, measurable improvements. Complemented with real-world case studies, this is an indispensable resource for software developers, quality and performance assurance engineers, architects, and managers. It is an ideal text for university courses related to computer and software performance evaluation, computer organization, and queuing theory.

Author Biography

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.

Table of Contents

Preface

Acknowledgments

Introduction: Performance versus Scalability

The Basics

Hardware Platform

Turning Machine

von Neumann Machine

Zuse Machine

Intel Machine

Sun Machine

System Under Test

Odds Against Turing

Sizing Hardware

Summary

Recommended Reading

Exercises

Software Platform

Software Stack

APIs

Multithreading

Categorizing Software

Enterprise Computing

Summary

Recommended Reading

Exercises

Testing Software Performance and Scalability

Scope of Software Performance and Scalability Testing

Software Development Performance

Defining Software Performance

Stochastic Nature of Software Performance Measurements

Amdahl's Law

Software Performance and Scalability Factors

System Performance Counters

Software Performance Data Principles

Summary

Recommended Reading

Exercises

Applying Queuing Theory

Introduction to Queuing Theory

Queuing Concepts and Metrics

Introduction to Probability Theory

Applying Probability Theory to Queuing Systems

Queuing Models for Networked Queuing Systems

Summary

Recommended Reading

Exercises

Case Study I: Queuing Theory to SOA

Introduction to SOA

XML Web Services

The Analytical Model

Service Demand

MedRec Application

MedRec Deployment and Test Scenario

Test Results

Comparing the Model with the Measurements

Validity of the SOA Performance Model

Summary

Recommended Reading

Exercises

Caser Study II: Queuing Theory Applied to Optimizing and Tuning Software Performance and Scalability

Analyzing Software Performance and Scalability

Effective Optimization and Tuning Techniques

Balanced Queuing System

Summary

Recommended Reading

Exercises

Applying Api Profiling

Defining API Profiling Framework

Defense Lines Against Software Performance and Scalability Defects

Software Program Execution Stack

The PerfBasic API Profiling Framework

Summary

Exercises

Enabling API Profiling Framework

Overall Structure

Global Parameters

Main Logic

Processing Files

Enabling Profiling

Processing Inner Classes

Processing Comments

Processing Methods Begin

Processing Return Statements

Processing Method End

Processing Main Method

Test Program

Summary

Recommended Reading

Exercises

Implementing API Profiling Framework

Graphics Tool-dot

Graphics Tool-ILOG

Graphics Resolution

Implementation

Summary

Exercises

Case Study: Applying API Profiting to Solving Software Performance and Scalability Challenges

Enabling API Profiling

API Profiling with Standard Logs

API Profiling with Custom Logs

API Profiling with Combo Logs

Applying API Profiling to Solving Performance and Scalability Problems

Summary

Exercises

Stochastic Equilibrium and Ergodicity

Basic Concepts

Classification of Random Processes

Discrete-Time Markov Chains

Continuous-Time Markov Chains

Stochastic Equilibrium and Ergodicity

Birth-Death Chains

Memoryless Property of The Exponential Distribution

M/M/1 Queues at Steady State

Review of Birth-Death Chains

Utilization and Throughput

Average Queue Length in the System

Average System Time

Average Wait Time

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