Solving Enterprise Applications Performance Puzzles Queuing Models to the Rescue

Langue : Anglais

Auteur : Grinshpan Leonid

Couverture de l’ouvrage Solving Enterprise Applications Performance Puzzles

Résumé
Sommaire
Biographie

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

Leonid Grinshpan Ph.D. (Stamford, CT) has been working for Oracle Corporation for more than 14 years as Consulting Technical Director. His customers include Dell, Citibank, Verizon, Clorox, Bank of America, AT&T, Best Buy, Aetna and many other major enterprises. He obtained Ph.D. in queuing models of computer systems from Russian Academy of Science.