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

To evaluate the average performance, 10 independent experiments with different
scientific workflow sizes (ranging from 2,000 to 50,000 workflow activities with
their mean activity durations between 30 to 3,000 time units) are executed 100 times
each. All the activity durations are generated by the normal distribution model. The
standard deviation is set as 10% of the mean activity duration to represent dynamic
system performance. We have also implemented other representative distribution
models such as exponential, uniform and a mixture of them. Since the experimental
results are similar, this chapter only demonstrates the results with normal
distribution.
The constraint setting utilises the strategy introduced in [55] and the initial
probability is set reasonably as 90% to serve as a type of QoS contract between
users and service providers which is agreed at scientific workflow build time. Here
the initial probability means that a scientific workflow has a 90% probability to
finish on time, or in other words, 90% workflow instances can finish on time.
Therefore, in our experiment, we specify the “satisfactory temporal correctness” as a
temporal violation rate below 10% so as to meet the QoS contract. The average
length of the workflow segments is set as 20 which is a moderate size for a
workflow sub-process similar to those high-level activities depicted in Figure 1.1.
Meanwhile, although under normal circumstances, the experiments can be
conducted without noises (i.e. 0% noise), to simulate some worse case scenarios,
random noises (i.e. a fixed rate of delays at random activities) are also injected to
simulate extra delays accordingly along workflow execution due to potential
unpredictable causes such as system overload and resource unavailability. For the
comparison purpose, four rounds of simulation experiments with different random
noise levels of 0%, 5%, 15% and 25% are conducted. Since the standard deviation
of activity durations are set as 10% of the mean values, according to the “ 3 σ ” rule,
there is a scarce chance that the noises, i.e. the delays, would exceed 30% of the
mean durations [87]. Therefore, we set the upper bound of noises as 25% to
investigate the effectiveness of our strategy under extreme situations. There are
many temporal violation handling strategies available as will be introduced in
Chapter 8. In our experiments, similar to the settings in Section 6.3.1, we use pseudo
workflow local rescheduling with a reasonable average time compensation rate of
50% [56, 100]. The size of rescheduled workflow activities is randomly selected up
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