Capacity Planning - Sun Microsystems Sun GlassFish Enterprise Server 2.1 Tuning Manual

General Tuning Concepts
Factors That Affect Performance
TABLE 1–2
Concept In practice
User Load Concurrent
sessions at
peak load
Application Transaction
Scalability rate measured
on one CPU
Vertical Increase in
scalability performance
from
additional
CPUs
Horizontal Increase in
scalability performance
from
additional
servers
Safety Margins High
availability
requirements
Excess capacity
for unexpected
peaks

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