SEKE 2012 Proceedings - Knowledge Systems Institute

TABLE IV
THE ESTIMATED HARDWARE REQUIREMENTS FOR EACH OF THE APPLICATIONS.
Application CPU requirement (MHz) Bandwidth peak Bandwidth daily Storage requirement
1 394.779 3.528 57.022 75.70
2 34.784 1.953 31.572 6.67
3 15.619 0.268 4.339 2.995
Total 445.183 5.750 92.933 85.365
TABLE V
THE FINAL COST FOR DEPLOYING THE APPLICATIONS.
Final Cost
Application CPU cost ($) Bandwidth cost ($) Storage cost ($) Total
1 20.134 17.106 28.955 66.195
2 1.774 9.427 2.551 13.797
3 0.979 1.302 1.146 3.244
Total ($) 22.704 27.880 32.652 83.236
the comparison would have fared with more people. Also, the
evaluation was targeted against mobile applications, furthering
limiting the comparison.
The data at our disposal was able to give us the total number
of unique users for the application, the user’s activity pattern
and the average size for each request. We were, however,
unable to use the data to estimate parameters such as the user
size (US) and various attributes in the Software Model such
as the request cost.
V. LIMITATIONS
There are several limitations with this model. As for now the
model does not help us to predict the QoS directly, as it simply
models the hardware requirements and their cost. Also, the
server’s storage and network costs are not that accurate when
it comes to the final cost since many service providers use
predefined templates for the hardware where a certain amount
of storage is already included in the price.
The requirements modeled by the Hardware Model does
not take any kind of growth into consideration. This is a
limitation when looking at the storage requirement. If we
were modeling a data intensive application, in which the users
are often uploading/downloading new content the storage will
grow and hence the required cost for it.
In order to estimate the parameters in the best possible
manner access to historical data is required. Also, different
applications may target different users and have different user
profiles as well, so in order for the historical data to be useful
it must contain data from a similar application type.
The CPU requirement are modeled after R max , the maximum
numbers of users expected within one hour. This approach
is a bit crude, a more realistic approach would be
to model the arrival rate within each hour using a suitable
probability distribution and then derive and use the mean or
maximum arrival rate as R max .
VI. CONCLUSIONS
In this study we have presented a unified model that can
be used to predict the workload, hardware requirements and
operational costs for a server infrastructure. The models are
designed with simplicity in mind and they are easily implemented
in a spreadsheet making the results easy to share.
Given a simple calibration of the model, the results are
comparable to expert opinions. The result can also be used
to compare the cost of service providers, and distribute applications
based on their hardware requirements. In the end,
these models can be used to help us make a more educated
guess when we are performing capacity planning in order to
assure cost-effective QoS.
We have conducted a static evaluation of the model. The
evaluation has been focused on determining what the impact
the model can have. Our future work will be focused on:
a) Addressing some of the issues as described in Section V
and also b) investigate the possibilities of adding an easy to use
QoS model and finally c) validate the models from a business
perspective
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