Xiao Liu PhD Thesis.pdf - Faculty of Information and Communication ...

Figure 6.2 Checkpoint Selection with Different Noises
The checkpoint selection results with different distribution models are depicted
in Figure 6.3. The result with 100% normal distribution models is also included as a
benchmark. Similarly to Figure 6.2, the number of checkpoint is growing
accordingly with the increase of workflow size. However, given different
combinations with different percentages of the three distribution models, the number
of selected checkpoints is very close to each other. For example, when the number
of workflow activities is 6,000, the numbers of checkpoints are 298, 294, 293, 292,
286 and 287 for 100%Norm, MIX(50%Norm, 30%Uni, 20%Exp), MIX(40%Norm,
40%Uni, 20%Exp), MIX(30%Norm, 40%Uni, 30%Exp), MIX(20%Norm, 40%Uni,
40%Exp) and MIX(10%Norm, 50%Uni, 40%Exp) respectively. The maximum
difference is 11. Therefore, even if we regard that all the activity durations follow
normal distribution models, the lowest accuracy rate of our checkpoint selection
strategy is 1-(11/298)=0.96, i.e. 96%. Specifically, the lowest accuracy rates for the
10 scientific workflows with workflow sizes ranging from 200 to 10,000 are 91%,
93%, 96%, 95%, 96%, 96%, 95%, 96%, 95% and 96%. Therefore, given our
checkpoint selection strategy, it can be observed that when the workflow size is
large enough, the accuracy rate is stable no matter what the actual underlying
distribution models are. However, since the real world system environments
normally cannot be simulated exactly by a single distribution model or even several
mixed distribution models, the experiments here cannot evaluate our strategy under
all possible circumstances but selected representatives. The idea here is to
demonstrate that the performance of our strategy will not be affected significantly by
the underlying distribution models, although it is built on the normal distribution
models.
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