Sun Microsystems 820434310 manual Capacity Planning, 2Factors That Affect Performance

General Tuning Concepts

TABLE 1–2Factors That Affect Performance

Concept	In practice	Measurement	Value sources
User Load	Concurrent	Transactions Per Minute (TPM)	(Max. number of concurrent users) * (expected response time) /
	sessions at	Web Interactions Per Second	(time between clicks)
	peak load
		(WIPS)	Example:
			(100 users * 2 sec) / 10 sec = 20
Application	Transaction	TPM or WIPS	Measured from workload benchmark. Perform at each tier.
Scalability	rate measured
	on one CPU
Vertical	Increase in	Percentage gain per additional	Based on curve fitting from benchmark. Perform tests while
scalability	performance	CPU	gradually increasing the number of CPUs. Identify the “knee” of
	from		the curve, where additional CPUs are providing uneconomical
	additional		gains in performance. Requires tuning as described in this guide.
	CPUs		Perform at each tier and iterate if necessary. Stop here if this
			meets performance requirements.
Horizontal	Increase in	Percentage gain per additional	Use a well-tuned single application server instance, as in
scalability	performance	server process and/or hardware	previous step. Measure how much each additional server
	from	node.	instance and hardware node improves performance.
	additional
	servers
Safety Margins	High	If the system must cope with	Different equations used if high availability is required.
	availability	failures, size the system to meet
	requirements	performance requirements
		assuming that one or more
		application server instances are
		non functional
	Excess capacity	It is desirable to operate a server	80% system capacity utilization at peak loads may work for most
	for unexpected	at less than its benchmarked	installations. Measure your deployment under real and
	peaks	peak, for some safety margin	simulated peak loads.

Capacity Planning

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.

24 Sun GlassFish Enterprise Server 2.1 Performance Tuning Guide • January 2009