The previous discussion guides you towards defining a deployment architecture. However, you determine the actual size of the deployment by a process called capacity planning. Capacity planning enables you to predict:
The performance capacity of a particular hardware configuration.
The hardware resources required to sustain specified application load and performance.
You can estimate these values through careful performance benchmarking, using an application with realistic data sets and workloads.
Determine performance on a single CPU.
First determine the largest load that a single processor can sustain. You can obtain this figure by measuring the performance of the application on a single-processor machine. Either leverage the performance numbers of an existing application with similar processing characteristics or, ideally, use the actual application and workload in a testing environment. Make sure that the application and data resources are tiered exactly as they would be in the final deployment.
Determine vertical scalability.
Determine how much additional performance you gain when you add processors. That is, you are indirectly measuring the amount of shared resource contention that occurs on the server for a specific workload. Either obtain this information based on additional load testing of the application on a multiprocessor system, or leverage existing information from a similar application that has already been load tested.
Running a series of performance tests on one to eight CPUs, in incremental steps, generally provides a sense of the vertical scalability characteristics of the system. Be sure to properly tune the application, Application Server, backend database resources, and operating system so that they do not skew the results.
Determine horizontal scalability.
If sufficiently powerful hardware resources are available, a single hardware node may meet the performance requirements. However for better availability, you can cluster two or more systems. Employing external load balancers and workload simulation, determine the performance benefits of replicating one well-tuned application server node, as determined in step (2).