Solving Enterprise Applications Performance Puzzles

Poorly performing enterprise applications are the weakest links in a corporation's management chain, causing delays and disruptions of critical business functions. This groundbreaking book frames enterprise application performance engineering not as an art but as applied science built on model-based methodological foundation. The book introduces queuing models of enterprise application that visualize, demystify, explain, and solve system performance issues. Analysis of these models will help to discover and clarify unapparent connections and correlations among workloads, hardware architecture, and software parameters.

Acknowledgments ix Preface xi 1. Queuing Networks as Applications Models 1 1.1. Enterprise Applications—What Do They Have in Common? 1 1.2. Key Performance Indicator—Transaction Time 6 1.3. What Is Application Tuning and Sizing? 8 1.4. Queuing Models of Enterprise Application 9 1.5. Transaction Response Time and Transaction Profile 19 1.6. Network of Highways as an Analogy of the Queuing Model 22 Take Away from the Chapter 24 2. Building and Solving Application Models 25 2.1. Building Models 25 Hardware Specification 26 Model Topology 28 A Model’s Input Data 29 Model Calibration 31 2.2. Essentials of Queuing Networks Theory 34 2.3. Solving Models 39 2.4. Interpretation of Modeling Results 47 Hardware Utilization 47 Server Queue Length, Transaction Time, System Throughput 51 Take Away from the Chapter 54 3. Workload Characterization and Transaction Profiling 57 3.1. What Is Application Workload? 57 3.2. Workload Characterization 60 Transaction Rate and User Think Time 61 Think Time Model 65 Take Away from the Think Time Model 68 Workload Deviations 68 “Garbage in Garbage out” Models 68 Realistic Workload 69 Users’ Redistribution 72 Changing Number of Users 72 Transaction Rate Variation 75 Take Away from “Garbage in Garbage out” Models 78 Number of Application Users 78 User Concurrency Model 80 Take Away from User Concurrency Model 81 3.3. Business Process Analysis 81 3.4. Mining Transactional Data from Production Applications 88 Profiling Transactions Using Operating System Monitors and Utilities 88 Application Log Files 90 Transaction Monitors 91 Take Away from the Chapter 93 4. Servers CPUs and Other Building Blocks of Application Scalability 94 4.1. Application Scalability 94 4.2. Bottleneck Identification 95 CPU Bottleneck 97 CPU Bottleneck Models 97 CPU Bottleneck Identification 97 Additional CPUs 100 Additional Servers 100 Faster CPUs 100 Take Away from the CPU Bottleneck Model 104 I/O Bottleneck 105 I/O Bottleneck Models 106 I/O Bottleneck Identification 106 Additional Disks 107 Faster Disks 108 Take Away from the I/O Bottleneck Model 111 Take Away from the Chapter 113 5. Operating System Overhead 114 5.1. Components of an Operating System 114 5.2. Operating System Overhead 118 System Time Models 122 Impact of System Overhead on Transaction Time 123 Impact of System Overhead on Hardware Utilization 124 Take Away from the Chapter 125 6. Software Bottlenecks 127 6.1. What Is a Software Bottleneck? 127 6.2. Memory Bottleneck 131 Memory Bottleneck Models 133 Preset Upper Memory Limit 133 Paging Effect 138 Take Away from the Memory Bottleneck Model 143 6.3. Thread Optimization 144 Thread Optimization Models 145 Thread Bottleneck Identification 145 Correlation Among Transaction Time, CPU Utilization, and the Number of Threads 148 Optimal Number of Threads 150 Take Away from Thread Optimization Model 151 6.4. Other Causes of Software Bottlenecks 152 Transaction Affinity 152 Connections to Database; User Sessions 152 Limited Wait Time and Limited Wait Space 154 Software Locks 155 Take Away from the Chapter 155 7. Performance and Capacity of Virtual Systems 157 7.1. What Is Virtualization? 157 7.2. Hardware Virtualization 160 Non-Virtualized Hosts 161 Virtualized Hosts 165 Queuing Theory Explains It All 167 Virtualized Hosts Sizing After Lesson Learned 169 7.3. Methodology of Virtual Machines Sizing 171 Take Away from the Chapter 172 8. Model-Based Application Sizing: Say Good-Bye to Guessing 173 8.1. Why Model-Based Sizing? 173 8.2. A Model’s Input Data 177 Workload and Expected Transaction Time 177 How to Obtain a Transaction Profile 179 Hardware Platform 182 8.3. Mapping a System into a Model 186 8.4. Model Deliverables and What-If Scenarios 188 Take Away from the Chapter 193 9. Modeling Different Application Configurations 194 9.1. Geographical Distribution of Users 194 Remote Office Models 196 Users’ Locations 196 Network Latency 197 Take Away from Remote Office Models 198 9.2. Accounting for the Time on End-User Computers 198 9.3. Remote Terminal Services 200 9.4. Cross-Platform Modeling 201 9.5. Load Balancing and Server Farms 203 9.6. Transaction Parallel Processing Models 205 Concurrent Transaction Processing by a Few Servers 205 Concurrent Transaction Processing by the Same Server 209 Take Away from Transaction Parallel Processing Models 213 Take Away from the Chapter 214 Glossary 215 References 220 Index 223