TESI DOCTORAL - La Salle

3.3. Deterministic hierarchical consensus architectures
% correct predictions
100
80
60
40
1 5 10
CP
S
15
CP
P
20
c : number of consensus processes
(a) Percentage of correct optimal
consensus architecture
predictions
RT (sec.)
15
10
5
RT S
RT P
0
1 5 10 15 20
c : number of consensus processes
(b) Absolute running time
differences between the truly
and allegedly optimal consensus
architectures
relative % RT
80
60
40
20
relative RT S
relative RT P
0
1 5 10 15 20
c : number of consensus processes
(c) Relative running time differences
between the truly and
allegedly optimal consensus
architectures
Figure 3.14: Evolution of the accuracy of the serial and parallel DHCA running time estimation
as a function of the number of consensus processes used in the estimation, measured in
terms of (a) the percentage of correct predictions, the (b) absolute and (c) relative running
time deviations between the truly and allegedly optimal consensus architectures.
ues of c does not imply significant reductions in ΔRT, which shows a pretty stationary
behaviour for c>5. Last, it is worth observing that, in the parallel case, the correct
prediction percentage increase and relative ΔRT decrease obtained for c>1resultinalmost
negligible absolute ΔRT reductions, which again reveals the lesser importance of the
aprioriselection of a particular parallel DHCA variant.
This adds to the fact that, as suggested earlier, it seems unnecessary to conduct any
runnning time estimation process for determining the fastest hierarchical consensus architecture
variant, as assigning the diversity factors to DHCA stages in decreasing cardinality
order apparently gives rise to the serial DHCA variant of minimum time complexity. With
the purpose of evaluating the validity of this latter hypothesis, we have conducted the
experiments presented throughout the following paragraphs.
Evaluation of the optimal DHCA prediction methodology based on decreasing
cardinality diversity factor ordering
As far as the serial DHCA implementation is concerned, we have computed the percentage
of experiments where the minimum real running time is achieved by the variant corresponding
to the decreasing cardinality ordering of the diversity factors. Moreover, in case this
prediction fails, we have computed the running time overhead resulting from selecting a
computationally suboptimal hierarchical consensus architecture as the fastest one. The average
results obtained for each one of the eight unimodal data collections are presented in
table 3.8.
It can be observed, for instance, that the DHCA variants defined by the ordered list of
diversity factors in decreasing cardinality order is always the fastest hierarchical consensus
architecture in the Zoo data set —which is equivalent to a 100% correct prediction rate.
Using this prediction method, the lowest accuracy is obtained in the MFeat data collection
(71.4%) —in this case, the average running time deviation derived from the 28.6% of in-
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