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

handling strategies employed in a scientific cloud workflow system. In such a case,
it is unclear which temporal violation handling strategy should be applied when a
certain level of temporal violation is detected, and thus will probably result in the
failure of temporal violation handling (for example, when a light-weight temporal
violation handling strategy is applied for a major temporal violation) or the loss of
unnecessary time and budget (for example, when a heavy-weight temporal violation
handling strategy is applied for a minor temporal violation). Therefore, for the
purpose of cost effectiveness, the definition of temporal violations should be able to
facilitate the selection of light-weight temporal violation handling strategies (for
minor temporal violations) and heavy-weight temporal violation handling strategies
(for major temporal violations).
2.3 Temporal Constraint Setting
Temporal constraint setting is to assign temporal constraints in scientific cloud
workflow specifications at build time [16]. Recent studies hold the assumption that
temporal constraints are pre-defined and focus only on runtime monitoring of
temporal consistency states. However, the fact is that efforts put at runtime will be
mostly in vain without build time setting of high quality temporal constraints. The
reason is obvious since the purpose of temporal verification is to identify potential
violations of temporal constraints to minimise the temporal violation handling cost.
Therefore, if temporal constraints are of low quality themselves, temporal violations
may often take place no matter how much efforts have been dedicated by runtime
temporal verification. In the real world, service users normally have limited
knowledge about the performance of the underlying cloud services except their
general descriptions. In such a case, it is difficult for users to estimate the activity
durations or workflow makespan. Therefore, the temporal constraints assigned by
service users are mainly based on their own interests with ignorance of the real
system performance such as system load [54]. Meanwhile, service users usually
prefer to assign only one or several coarse-grained temporal constraints. However,
for the purpose of effectively monitoring a large scale scientific cloud workflow, a
large number of fine-grained temporal constraints are required along cloud
workflow execution. Therefore, scientific cloud workflow systems need to derive
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