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

candidate probability temporal consistency states one after another, e.g. 88%, 85%
and 83%, and the service provider replies with 6445s, 6415s and 6397s as the
corresponding temporal consistency states. Since 6397s is lower than the 6400s
maximum threshold, through probability oriented negotiation process, the final
negotiation result could be an upper bound temporal constraint of 6397s with a
probability temporal consistency state of 83%. Evidently, from this example, with
the result of 6380s and 6397s obtained through two different negotiation processes,
we can confirm that the setting process is effective no matter which kind of
negotiation process is adopted. Furthermore, the final coarse-grained temporal
constraints obtained are normally similar if the decision maker is the same user. The
setting result of time oriented negotiation process is presented in Table 5.4.
Table 5.4 Setting Results
The third step is to set the fine-grained temporal constrains for each workflow
activity with the obtained overall upper bound constraint. As mentioned in Section
5.2.2, the probability based temporal consistency defines that the probability for
each expected activity duration is the same as the probability consistency state of the
workflow process. Therefore, take the result obtained through the time oriented
negotiation process for illustration, since the coarse-grained temporal constraint is
6380s with a probability consistency state of 81%, according to Formula 5.2, the
fine-grained temporal constraints for each activity can be obtained instantly.
n
n
n
2 2
Since ∑ wiσ i = 412 , ∑ wi
σ i = 217 and ∑σ
i = 250
i=
1
i=
1
i=
1
, the coefficient here is
( 412 217) 250
1− − which equals to 0.22. Therefore, for example, the fine-grained
upper bound temporal constraint for activity X 1 is
( 105 + 0.88* 225 *0.22) = 108s
88