Server User Manual

TABLE 1–2 Factors That Aect Performance
Concept In practice Measurement Value sources
User Load Concurrent
sessions at
peak load
Transactions Per Minute (TPM)
Web Interactions Per Second
(WIPS)
(Max. number of concurrent users) * (expected response time) /
(time between clicks)
Example:
(100 users*2sec)/10sec=20
Application
Scalability
Transaction
rate measured
on one CPU
TPM or WIPS Measured from workload benchmark. Perform at each tier.
Vertical
scalability
Increase in
performance
from
additional
CPUs
Percentage gain per additional
CPU
Based on curve tting 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.
Horizontal
scalability
Increase in
performance
from
additional
servers
Percentage gain per additional
server process and/or hardware
node.
Use a well-tuned single application server instance, as in
previous step. Measure how much each additional server
instance and hardware node improves performance.
Safety Margins High
availability
requirements
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
Dierent equations used if high availability is required.
Excess capacity
for unexpected
peaks
It is desirable to operate a server
at less than its benchmarked
peak, for some safety margin
80% system capacity utilization at peak loads may work for most
installations. Measure your deployment under real and
simulated peak loads.
Capacity Planning
The previous discussion guides you towards dening 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 conguration.
■
The hardware resources required to sustain specied application load and performance.
You can estimate these values through careful performance benchmarking, using an
application with realistic data sets and workloads.
General Tuning Concepts
Sun GlassFish Enterprise Server 2.1 Performance Tuning Guide • January 200924