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

service user’s requirements and the system’s performance. For such a purpose, with
the probability based temporal consistency model, a win-win negotiation process
between service users and service providers is designed to support the setting of
coarse-grained temporal constraints. The negotiation process can be conducted
either in a time-oriented way where the service user suggests new temporal
constraints and the service provider replies with the corresponding probability
consistency state (i.e. probability confidence for completing the workflow instance
within the specified temporal constraint), or in a probability-oriented way where the
service user suggests new probability consistency state and the service provider
replies with the corresponding temporal constraints. Finally, a set of balanced
coarse-grained temporal constraints can be achieved. Details about the setting
strategy for coarse-grained temporal constraints will be presented in Chapter 5.
The third step is to set fine-grained temporal constraints. Given the results of the
previous step, coarse-grained temporal constraints need to be propagated along the
entire workflow instance to assign fine-grained temporal constraints for each
workflow activity. In our strategy, an automatic propagation process is designed to
assign fine-grained temporal constraints based on their aggregated coarse-grained
ones. A case study demonstrates that our setting strategy for fine-grained temporal
constraint is very efficient and accurate. Details about the setting strategy for finegrained
temporal constraints will also be presented in Chapter 5.
3.3 Component 2: Temporal Consistency Monitoring
Component 2 is temporal consistency monitoring. As depicted in Table 3.2, the
input includes temporal constraints assigned by Component 1 at workflow build
time and runtime workflow activity durations for completed activities and predicted
activity duration intervals for those not-yet-commenced activities (based on the
forecasting strategy in Component 1). The output is mainly on the current temporal
consistency state at a selected activity point, i.e. checkpoint. The target of this
component is to keep the cloud workflow execution under constant monitoring and
detect potential temporal violations as early as possible.
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