Software Performance and Scalability

Transcription

1 Software Performance and Scalability A Quantitative Approach Henry H. Liu ^ IEEE )computer society WILEY A JOHN WILEY & SONS, INC., PUBLICATION

2 Contents PREFACE ACKNOWLEDGMENTS xv xxi Introduction 1 Performance versus Scalability / 1 PART 1 THE BASICS 3 1. Hardware Platform Turing Machine / von Neumann Machine / Zuse Machine / Intel Machine / History of Intel's Chips / Hyperthreading / Intel's Multicore Microarchitecture / Challenges for System Monitoring Tools / Sun Machine / System Under Test / Processors / Motherboard / Chipset / 20 VII

3 VIII CONTENTS Storage / RAID / Networking / Operating System / Odds Against Turing / Memory Leaks / SLAs / Sizing Hardware / Summary / 37 Recommended Reading / 37 Exercises / Software Platform 2.1 Software Stack / APIs / Windows APIs / Java APIs / Google APIs / Multithreading / Categorizing Software / Systems Software / Application Software / Middleware Software / Enterprise Computing / What Is Enterprise Software? / Enterprise Software Architecture / Monolithic Architecture / Client/Server Architecture / Three-Tier Architecture / N-Tier Architecture / Software Componentry / Service-Oriented Architecture / Summary / 63 Recommended Reading / 64 Exercises / Testing Software Performance and Scalability 3.1 Scope of Software Performance and Scalability Testing / Performance Regression Testing / Performance Optimization and Tuning Testing / Performance Benchmarking Testing /

4 CONTENTS ix Scalability Testing / QA Testing Versus Performance Testing / Additional Merits of Performance Testing / Software Development Process / Agile Software Development / Extreme Programming / Defining Software Performance / Performance Metrics for OLTP Workloads / Performance Metrics for Batch Jobs / Stochastic Nature of Software Performance Measurements / Amdahl's Law / Software Performance and Scalability Factors / Hardware / Operating System / Database Statistics / SQL Server Parameterization / Database Deadlocks / Licensing / System Performance Counters / Windows Performance Console / Using perfmon to Diagnose Memory Leaks / Using perfmon to Diagnose CPU Bottlenecks / Using perfmon to Diagnose Disk I/O Bottlenecks / Using Task Manager to Diagnose System Bottlenecks / UNIX Platforms / Software Performance Data Principles / Summary /131 Recommended Reading / 132 Exercises / 133 PART 2 APPLYING QUEUING THEORY Introduction to Queuing Theory Queuing Concepts and Metrics / Basic Concepts of Queuing Theory / Queuing Theory: From Textual Description to Mathematical Symbols / Introduction to Probability Theory / Random Variables and Distribution Functions / Discrete Distribution and Probability Distribution Series / 144

5 CONTENTS Continuous Distribution and Distribution Density Function / Applying Probability Theory to Queuing Systems / Markov Process / Poisson Distribution / Exponential Distribution Function / Kendall Notation / Queuing Node versus Queuing System / Queuing Models for Networked Queuing Systems / Queuing Theory Triad I: Response Time, Throughput, and Queue Length (Little's Law) / M/M/l Model (Open) / Queuing System: With Feedback versus Without Feedback / Queuing Theory Triad II: Utilization, Service Time, and Response Time / Multiple Parallel Queues versus Single-Queue Multiple Servers / M/M/m/N/N Model (Closed) / Finite Response Time in Reality / Validity of Open Models / Performance and Scalability Bottlenecks in a Software System / Genealogy of Queuing Models / Summary / 172 Recommended Reading / 174 Exercises / Case Study I: Queuing Theory Applied to SOA 5.1 Introduction to SOA / XML Web Services / The Analytical Model / Service Demand / Web Services Handle Creation / XML SOAP Serialization/Deserialization / Network Latency / XML Web Service Provider / Database Server / Datastorage / MedRec Application / Exposing a Stateless Session EJB as an XML Web Service / Consuming an XML Web Service Using SOAP / MedRec Deployment and Test Scenario / 189

6 CONTENTS Xi 5.7 Test Results / Overhead of the XML Web Services Handle / Effects of Caching Web Services Handle / Throughput Dynamics / Bottleneck Analysis / Comparing the Model with the Measurements / Validity of the SOA Performance Model / Summary / 200 Recommended Reading / 201 Exercises / Case Study II: Queuing Theory Applied to Optimizing and Tuning Software Performance and Scalability Analyzing Software Performance and Scalability / Characterizing Performance and Scalability Problems / Isolating Performance and Scalability Factors / Applying Optimization and Tuning / Effective Optimization and Tuning Techniques / Wait Events and Service Demands / Array Processing Reducing V,- / Caching Reducing Wait Time (W,-) / Covering Index Reducing Service Demand (Д) / Cursor-Sharing Reducing Service Demand (Д) / Eliminating Extraneous Logic Reducing Service Demand (D,-) / Faster Storage Reducing Data Latency (Wj) / MPLS Reducing Network Latency (W t ) / Database Double Buffering An Anti Performance and Scalability Pattern / Balanced Queuing System / Summary / 244 Recommended Reading / 245 Exercises / 246 PART 3 APPLYING API PROFILING 7. Denning API Profiling Framework 7.1 Defense Lines Against Software Performance and Scalability Defects / Software Program Execution Stack / The Per/Basic API Profiling Framework /

7 xii CONTENTS API Profile Logging Format / Performance Log Parser / Performance Maps / Performance Summarization File / Summary / 260 Exercises / Enabling API Profiling Framework Overall Structure / Global Parameters / Main Logic / Processing Files / Enabling Profiling / Processing Inner Classes / Processing Comments / Processing Method Begin / Processing Return Statements / Processing Method End / Processing Main Method / Test Program / Summary / 279 Recommended Reading / 279 Exercises / Implementing API Profiling Framework Graphics Tool dot / Graphics Tool ILOG / Graphics Resolution / Implementation / driver / Global Parameters / logreader / logwriter / Node / Link / CallRecord / utility / parser / xmlprocessor / analyzer / adapter / Summary / 300 Exercises /301

8 CONTENTS ХШ 10. Case Study: Applying API Profiling to Solving Software Performance and Scalability Challenges Enabling API Profiling / Mechanism of Populating Log Entry / Source and Target Projects / Setting apf.properties File / Parsing Workflow / Verifying the Profiling-Enabled Source Code / Recommended Best Coding Practices / Enabling Non-Java Programs / API Profiling with Standard Logs / Generating API Profiling Log Data / Parsing API Profiling Log Data / Generating Performance Maps / Making Sense Out of Performance Maps / API Profiling with Custom Logs / Using Adapter to Transform Custom Logs / Generating Performance Maps with Custom Logs / API Profiling with Combo Logs / Client Side Performance Map / Server Side Performance Map / Applying API Profiling to Solving Performance and Scalability Problems / Baseline / Optimization / Analysis / Summary / 337 Exercises / 338 APPENDIX A STOCHASTIC EQUILIBRIUM AND ERGODICITY 339 A.l Basic Concepts / 339 A. 1.1 Random Variables / 339 A. 1.2 Random Variable Vector / 340 A. 1.3 Independent and Identical Distributions (IID) /341 A. 1.4 Stationary Processes / 342 A. 1.5 Processes with Stationary Independent Increments / 342

9 XIV CONTENTS A.2 Classification of Random Processes / 343 A.2.1 General Renewal Processes / 343 A.2.2 Markov Renewal Processes / 343 A.2.3 Markov Processes / 343 A.3 Discrete-Time Markov Chains / 345 A.3.1 Transition Probability Matrix and C-K Equations / 345 A.3.2 State Probability Matrix / 347 A.3.3 Classification of States and Chains / 348 A.4 Continuous-Time Markov Chains / 349 A.4.1 C-K Equations / 349 A.4.2 Transition Rate Matrix / 349 A.4.3 Imbedded Markov Chains / 350 A.5 Stochastic Equilibrium and Ergodicity /351 A.5.1 Definition / 351 A.5.2 Limiting State Probabilities / 353 A.5.3 Stationary Equations / 354 A.5.4 Ergodic Theorems for Discrete-Time Markov Chains / 354 A.5.5 Ergodic Theorems for Continuous-Time Markov Chains / 356 A.6 Birth-Death Chains / 357 A.6.1 Transition Rate Matrix / 357 A.6.2 C-K Equations / 358 A.6.3 Limiting State Probabilities / 359 A.6.4 Ergodicity / 359 APPENDIX В MEMORYLESS PROPERTY OF THE EXPONENTIAL DISTRIBUTION 361 APPENDIX С М/М/1 QUEUES AT STEADY STATE 363 C.l Review of Birth-Death Chains / 363 C.2 Utilization and Throughput / 364 C.3 Average Queue Length in the System / 365 C.4 Average System Time / 365 C.5 Average Wait Time / 366 INDEX 367