Download Graphic Profiling of User Group Activity

Profiling User Group Activity With SAS® Software
H. Pat Artis
Morino Associates
Vienna, VA 22180
the user's daily activities. The reader should note that this same
technique could be used to quantify activity at other levels of
granularity like hourly, weekly, or monthly observations,
1.0 Introduction
One of the most common requests made to CPE analysts respon~
sible for performance reporting by their management is to develop
report formats that highlight or easily identify significant changes
As a measure of a significant difference, the standard deviation
provides an adaptive measure that allows us to quantify change.
To accomplish this objective, one need only calculate the Zstatistic for the observation to be evaluated. Simply described, a
Z-statistic is a measure of how many important differences a
measure is larger or smaller than the mean of the population form
which it was selected, The Z-statistic is calculated:
that occur in an environment. For the performance analyst to
understand tlie managers motivation for asking for such reports, it
is important to remember that the one of the most basic concepts
of management is the by control and understanding changes.
Unfortunately, the development of such reports is not a trivial task.
To quantify and identify changes, the analyst must first determine
what magnitude of variance of a given measurement is a sign of a
significant change, and second present the information in a form
that is easily understood by the recipient of the report,
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One other results of the Z-transformation is that the range of
Z-statistics Over which Z can be expected to vary is well understood. For most cases (i.e., 99%+ of all observations), the resulting Z~statistics can be expected to range between -3.5 and + 3.5.
Negative Z-statistics correspond to values smaller than the mean
and positive Z-statistics correspond to observation largerthan the
mean. Thus, the sign of the Z~statistic provides an easily understood indicator of the nature of the change that occurred. For the
daily observations of transaction volume introduced above, Friday's observation is 0.97 standard deviations greater than the five
day mean. While not an overwhelming difference, it is certainly a
change in the nature of the system's workload worthy of note
In this paper, we will examine a graphical reporting tool that is
based on the use of the Z-statistic to address these management
reporting requirements. The quantification of the activity of TSO
user groups will be used as an example for this discussion.
2.0 Quantifying Changes
To quantify changes, the analyst must select or identify threshold
values which indicate significant changes in the selected
measurements. For any given measurement, the experienced
analyst can likely select a threshold value to evaluate changes.
Unfortunately, when a large number of measurements are to be
included in a single report or when a given report format is to be
produced for a large community of users, the maintenance of
tables of such threshold values becomes an unacceptable task.
Failure to maintain such tables of values has historically been one
of the most common reasons that complex exception reporting
schemes have failed to realize their potential.
One additional benefit of the Z-statistiCs is that they do not require
the recipient of the report to have any knowledge of the prior values
that have defined the measure in question, Rather, the Z-statistic
provides a simple one number indicator of change that quantifies
its direction and magnitude relative to the past history for the user
in question.
Rather than attempting to maintain a table of threshold values for
every measurement or group of users, it is desirable to develop an
adaptive means of identifying significant changes on a user group
andlor measurement basis. Fortunately, simple central tendency
statistics provides us with an adaptive means of identifying such
changes calted the standard deviation. Consider the following
daily transaction counts for a given user:
Monday
Tuesday
Wednesday
Thursday
Friday
3.0 Graphical Presentation
Using the Z-statistics discussed in the prior section, simplegraphical reports can be developed to highlight the changes in selected
workloads. A sample report employing this notion is shown in
Figure 1. The development ofthe report is discussed in the following paragraphs.
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2,053
2,290
For the sample study, the activity of groups of TSO users at a
weekly level was selected for study using data collected with
TSO/MON, User groups were identified in this study by the first
three characters of the TSO LOGON ID recorded by the measurement tool. Three metrics were selected to profile the users in this
case study. They were:
To identify if Friday represented a significant change in the users
activity, we must first determine what is normal for that user on a
daily basis, Calculating the mean and standard deviation we find:
X
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- X
Z(i)
average T30 short response time,
total CPU time, and
total TSO transactions.
Where the mean provides us a measure of the central tendency for
the user and the standard deviation provides us an indication of
"what a significant difference" is for the observations that quantify
The selection of these variables was a matter of convenience and
do not represent a suggested set that should be considered for
194
any group of T80 users.
While this case study concentrated on T80 measurements, the
same reporting structure could include any other workload or
service measurements that can be summarized on the basis of
user group. For example, batch job statistics, network service
levels, and DA8D consumption could easily be computed and
included in a single page report for each group of users.
To calculate the mean and standard deviation values, the activity
for the T80 user groups was summarized at a weekly level. The
most recent ten observations (i.e., last ten weeks) for each user
group was maintained in a 8AS dataset. The selection of the
number of past observations to maintain is a key decision since the
volatility (and hence the ability to quickly identify change) of the
mean and standard deviation values tends to decrease as the the
number of observations grows larger.
4.0 Remarks
A simple method for quantifying and graphically profiling the
changes in User group and/or workload activities has been presented. Using this technique, the analyst can implement an adaptive reporting system that quantifies and identifies changes in
computer workloads.
Once the mean and standard deviations are calculated for the
measurements describing a given user, the Z-statistic value can
be calculated for the most recent observation. As shown in the
figure, this Z-statistic was then used to develop a simple box plot.
(Z-statistic values greater than 3.0 or less than -3.0 are truncated to
3.0 and -3.0 respectively in this report.) In addition to the box plot,
the most recent five weekly observations for the measurements are
also shown in the report.
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Figure 1. Sample Report for TSO Users
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