MNEMEE - Electronic Systems - Technische Universiteit Eindhoven

There is however one problem with using PCA to identify scenario parameters. This technique selects
a set of run-time parameters that can be used to compute the cost. However, it is not guaranteed that
this is the smallest set of run-time parameters. In fact, PCA may select a set of run-time parameters of
which part of the set has a linear relation with other run-time parameters that are also selected.
Keeping track of more scenario parameters at run-time may make the scenario detector more
computationally expensive. Therefore, the set of scenario parameters should be minimized. This can
be done by a dimension reduction technique known as feature subset selection [23] on the set of
scenario parameters found by the PCA. Feature subset selection algorithms are iterative algorithms
that typically consist of a filter and a wrapper. Starting from one or more initial candidates (e.g.
subsets of the scenario parameters as identified by the PCA), the filter uses an evaluation criterion to
decide which candidates are carried over to the next iteration of the algorithm. The wrapper technique
determines how features (i.e. scenario parameters) are added or removed from the set of candidates.
For this purpose, the wrapper uses an evaluation criterion to rate and compare different candidates.
The wrapper can be seen as a filter that selects the most important scenario parameters. To minimize
the set of scenario parameters, the feature subset algorithm can start with a set of candidates in which
one scenario parameter is removed from each candidate as compared to the parameters identified by
the PCA. Next, the algorithm can compute for each candidate a cost matrix C’ (similar to the matrix
C) that estimates the costs of each run-time situation when the run-time parameters that are selected
within this candidate are used. The wrapper selects the candidate with the lowest mean squared error
between the matrices C’ and C. The algorithm is then repeated starting from this new candidate. This
procedure is continued till no candidate can achieve the required accuracy. The result is a set of
scenario parameters of which no run-time parameter can be removed without impacting the accuracy
of the estimated cost of executing a run-time situation.
7.4.4. Scenario formation
The scenario parameter selection step has identified the run-time parameters that are needed to
accurately predict the cost of executing a run-time situation. The relation between the value of the runtime
parameters and the cost is captured in the cost-space model that is also generated by the scenario
parameter selection step. The scenario formation step uses this cost-space model to cluster run-time
situation with similar costs into a single system scenario. When clustering run-time situations, the cost
and frequency of scenario switches should be taken into account. For this purpose, the trace
information and platform characteristics are used in the scenario formation step.
So far, we have not selected a specific clustering algorithm has been selected for this step. However,
K-means clustering is considered as a promising candidate for this step. This technique has been
successfully used before to clustering run-time situations [31]. K-means clustering is a technique in
which the points in a trade-off space are clustered into K disjoint groups. Points are clustered together
based on a similarity criterion (e.g. Euclidean distance). The two main issues with using K-means
clustering are (1) to choose the number of clusters (i.e. the number of scenarios) and (2) to select the
initial points in the space that form the centre of the clusters. A strategy to make these two choices
might be to look at the histogram of one or more cost dimensions. The number of peaks in the
histogram determines the value of K. The peaks define the centre of the initial clusters. Furthermore,
the switching cost between scenarios should also be taken into account in the clustering. This can be
done by adding the switching cost as an additional dimension to the trade-off space.
Once the scenario formation is completed, a predictor must be created that at run-time predicts the
scenario that will be active in the foreseeable future. The technique proposed in [28] to construct a
run-time predictor for detecting a scenario can be used for this purpose. When constructing the
predictor a trade-off is made between which scenario parameters are actually observed at run-time and
how quickly a decision can be made on the next scenario that will be active.
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