Note: Code listings are numbered in a separate series in each chapter and gathered in their own subsection, following the figures.
Figure 1.1 Customers in a traditional bookstore
Figure 1.2 Customers using an on-line bookstore
Figure 1.3 Concurrent users in a traditional bookstore
Figure 1.4 Concurrent web site load
Figure 1.5 Active customers in a traditional bookstore
Figure 1.6 Active web site clients
Figure 1.7 Traditional store customers per hour
Figure 1.8 Throughput dynamics without waiting
Figure 1.9 Throughput dynamics with waiting
Figure 1.10 Throughput curve: brick and mortar store
Figure 1.11 Typical web site throughput curve
Figure 1.12 Total checkout time in a traditional store
Figure 1.13 Response time in an on-line store
Figure 1.14 Think time example
Figure 1.15 Response time queue for a brick and mortar store
Figure 1.16 Throughput curve and response time graph
Figure 1.17 Steady-state measurement interval
Figure 1.18 Reducing the checkout process path length
Figure 1.19 Example bottleneck: gift-wrap department
Figure 1.20 Traditional store handling more customers in parallel
Figure 1.21 Scaling with multiple bookstores
Figure 1.22 Horizontal scaling example
Figure 2.1 Model-View-Controller within a Java web application
Figure 2.2 Small servlets versus front controller strategy
Figure 2.3 Threading/queuing "funnel"
Figure 2.4 A frozen web site
Figure 2.5 Servlet loading versus JSP loading
Figure 2.6 Cookies and HTTP sessions
Figure 2.7 Failover with a persistent HTTP session database
Figure 2.8 Enterprise JavaBean transactions without a fa §ade Bean
Figure 2.9 Enterprise JavaBean transactions using a fa §ade Bean
Figure 2.10 Clones in both vertical and horizontal scaling
Figure 3.1 Router placement in a large web site
Figure 3.2 A typical web site firewall configuration
Figure 3.3 Reverse proxy server used for security
Figure 3.4 Caching reverse proxy servers
Figure 3.5 Different speeds for networks handling different loads
Figure 3.6 A load balancer distributing HTTP traffic
Figure 3.7 An example of load balancer affinity routing
Figure 3.8 Proxy server impacting even load distribution with affinity routing
Figure 3.9 Users accessing a web site through a proxy server farm
Figure 3.10 A simplified version of the SSL handshake
Figure 3.11 Plug-in operation
Figure 3.12 Vertical scaling of the application server
Figure 3.13 A small web site using horizontal scaling
Figure 3.14 A DMZ configuration with servlets, JSPs, and EJBs
Figure 4.1 Two examples of web site traffic patterns
Figure 4.2 A conceptualized JVM heap and corresponding settings
Figure 4.3 Typical garbage collection cycles
Figure 4.4 Garbage collecting too frequently
Figure 4.5 Typical memory leak pattern
Figure 5.1 A brokerage web site's traffic patterns
Figure 5.2 Yearly traffic patterns for an e-Commerce site
Figure 5.3 Web site configuration supporting access by pervasive devices
Figure 6.1 Peak versus average web site load
Figure 6.2 One HTML assembled from multiple HTTP requests
Figure 6.3 The same daily traffic volume spread over both 8- and 24-hour days
Figure 6.4 An example test environment
Figure 7.1 Java Pet Store demo home page
Figure 7.2 Java Pet Store hierarchy
Figure 7.3 Example of primitive scripts
Figure 7.4 LoadRunner parameter properties GUI
Figure 7.5 Result of Browse for Fish
Figure 7.6 Example of customizing think times
Figure 8.1 Script recording
Figure 8.2 Example LoadRunner ramp-up
Figure 8.3 Example of SilkPerformer V workload configuration
Figure 8.4 Testing environment
Figure 8.5 Sample LoadRunner Summary Report
Figure 8.6 Sample LoadRunner monitor
Figure 9.1 An example test environment
Figure 9.2 Shared network impact
Figure 9.3 Typical DMZ configuration for application servers
Figure 9.4 Single-disk versus multidisk database I/O
Figure 9.5 Testing live legacy systems
Figure 9.6 Master/slave load generator configuration
Figure 9.7 Testing the networks with FTP
Figure 9.8 One possible test ordering for a large test environment
Figure 11.1 Iterative test and tuning process
Figure 11.2 Test run measurement interval
Figure 11.3 Measuring over long runs to simulate steady state
Figure 11.4 Example of high run-to-run variation
Figure 11.5 Example of acceptable run-to-run variation
Figure 11.6 Example of run variation with a downward trend
Figure 11.7 Results after introducing a change
Figure 11.8 Throughput curve to determine saturation point
Figure 11.9 SMP scaling comparisons
Figure 11.10 Vertical scaling using multiple JVMs
Figure 11.11 Load balancing architecture options
Figure 11.12 Testing individual server performance
Figure 11.13 Quantifying load balancer overhead
Figure 11.14 Testing a two-server cluster
Figure 11.15 Two-server cluster scalability results
Figure 12.1 Sample vmstat output
Figure 12.2 Sample Microsoft Windows 2000 System Monitor
Figure 12.3 Sample JVM heap monitor
Figure 12.4 Sample Thread Analyzer screen capture
Figure 12.5 Sample Resource Analyzer output
Figure 13.1 Underutilization example
Figure 13.2 Example of bursty behavior
Figure 13.3 Example of a bursty back-end database system
Figure 13.4 Example of high CPU utilization
Figure 13.5 Example of high system CPU utilization
Figure 13.6 Example of high CPU wait on a database server
Figure 13.7 Uneven cluster loading
Figure 13.8 IP-based versus user -based affinity
Figure 14.1 TriMont performance test configuration
Figure 15.1 Sample user ramp-up performance test results
Figure 15.2 User ramp-up performance test results
Figure 15.3 TriMont scalability results
Figure D.1 Summary test results graph
