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

conducted for three times, i.e. starting from the verification of strong consistency,
then weak consistency, then weak inconsistency, before the verification of strong
inconsistency [15]. Therefore, the cost of other three times of temporal verification
is actually unnecessary and should be avoided. In our strategy, with the probability
based temporal consistency model where continuous temporal consistency states are
defined, it is no longer necessary to verify different types of temporal consistency
states as one is sufficient to determine the actual level of temporal violations.
Specifically, each temporal consistency state is associated with a unique probability
value and it is either within the range of statistically recoverable or non-recoverable
temporal violations. Therefore, checkpoint selection and temporal verification are
required to be conducted only once at every activity in our temporal framework.
Details about the temporal verification strategy will be presented in Chapter 6.
3.4 Component 3: Temporal Violation Handling
The third component is temporal violation handling. At a selected temporal violation
handling point, temporal violation handling is to tackle the existing temporal
violations by reducing or ideally removing the occurred time deficits. As depicted in
Table 3.3, the input includes the necessary and sufficient checkpoints, temporal
consistency states and the current workflow scheduling plans (which define the
assignment of workflow activities to computing resources, e.g. virtual machines in
cloud computing environments). The output includes the temporal violation
handling points and regenerated workflow scheduling plans which can reduce or
ideally remove the occurred time deficits of violated workflow instances.
The First step is to select temporal violation handling points. In the conventional
temporal verification work [15, 17, 56], a temporal violation handling point is
regarded the same as a necessary and sufficient checkpoint in nature, i.e. temporal
violation handling should be triggered whenever a temporal violation is detected.
However, due to the dynamic nature of cloud workflow environments, the number
of selected checkpoints can still be very huge especially in large scale scientific
cloud workflow applications. Therefore, given the probability of self-recovery and
motivated by random testing techniques, an adaptive temporal violation handling
point selection strategy is designed to further select a subset of necessary and
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