A probabilistic strategy for temporal constraint management in scientific workflow systems

In scientific workflow systems, it is critical to ensure the timely completion of scientific workflows. Therefore, temporal constraints, as a type of QoS (Quality of Service) specification, are usually required to be managed in scientific workflow systems. Specifically, temporal constraint management includes two basic tasks: setting temporal constrains at workflow build time and updating temporal constraints at workflow run time. For constraint setting, the current work mainly adopts user specified temporal constraints without considering system performance. Hence, it may result in frequent temporal violations which deteriorate the overall workflow execution effectiveness. As for constraint updating which so far has not been well investigated, it is in fact of great importance to workflow management tasks such as workflow scheduling and exception handling. In this paper, with a systematic analysis of the above issues, we propose a probabilistic strategy for temporal constraint management which utilises a novel probability based temporal consistency model. Specifically, for constraint setting, a negotiation process between the client and the service provider is designed to support the setting of coarse-grained temporal constraints and then automatically derive the fine-grained temporal constraints; for constraint updating, the probability time deficit/redundancy propagation process is proposed to update run-time finegrained temporal constraints when workflow execution is either ahead of or behind the schedule. The effectiveness of our strategy is demonstrated through a case study on an example scientific workflow process in our scientific workflow system.