Stevenson, Christopher Eric

### Description

This thesis takes the form of a case-study. It
is concerned with the application of principal component
analysis and regression analysis to computer performance
data.
The problem considered was that of finding a way
to quantitatively assess the effect of changes in a
computer's scheduling parameters on its response. Two
variables (# responses for PQI/CMQ swapin, denoted by y(l),
and ticks for PQI/CMQ swapin response, denoted by y(2))
were taken as measures of response and a procedure...[Show more] devised
to assess, for a given load, the change in these variables
brought about by a change in the parameters.
In order to make such an assessment, a single
measure of load is required. There are 26 different measures
of the load on different parts of the system and these
measurements are recorded automatically every minute that
the system is operating. The data available for this study
was all these measurements for the months April, May and
June 1980. A subset of 1000 points was taken from the April
and May data and a principal component analysis done on
this subset. Some properties of the first principal
component were studied and this component was accepted as
an index of load.
The index was then used to divide points from
the April and May data set into groups of similar load and
regression equations relating some of the load variables to
each of y(l) and y(2) were then estimated for each group.
A satisfactory equation for y (1) could be found for each group but not for y(2). After a process of trial and error it was found that an equation for the transformed
variable Z = log(y(2)) could be found for most but not all
of the groups. Then it was found that the groups could be
further combined without significant loss of information
into three groups and that equations could be fitted for
y (1) to all three groups but for Z only to two of the
groups. The ability of these equations to predict response
from load was then tested by drawing a sample of points
from the June data and comparing the observed values of
y (1) and Z with the values predicted by the equations.
Thus the procedure to assess the effect of a
parameter change was to select a sample of points from
immediately after such a change and use the equations (which
have been estimated using data from before the change) to
predict the response variables. These predictions represent
estimates of the expected response for these points under
the old parameter settings and the observed values represent
the actual response under the new settings. Thus a
comparison between the observed and the actual response
gives a measure of the change in response brought about by
the parameter change.
Finally this procedure was applied to some data
taken from September 1980 (after a change in the parameter
values) and was found to perform satisfactorily.

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