Abstract

In scientific cloud workflow systems, temporal violation handling points are those workflow activity points where temporal violation handling strategies are triggered to tackle detected temporal violations. The existing work on temporal verification adopts the philosophy of temporal violation handling required whenever a temporal violation is detected. Therefore, a checkpoint is regarded the same as a temporal violation handling point. However, the probability of self-recovery which utilises the time redundancy of subsequent workflow activities to automatically compensate for the time deficit is ignored and hence would impose a high-temporal violation handling cost. To address such a problem, this book presents a novel adaptive temporal violation handling point selection strategy where the probability of self-recovery is effectively utilised in temporal violation handling point selection to avoid unnecessary handling for temporal violations. This chapter is organised as follows. Section 8.1 presents the specifically related work and problem analysis. Section 8.2 presents our novel adaptive temporal violation handling point selection strategy. Section 8.3 demonstrates the evaluation results.

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