Task Completion Prediction Method in Cloud Scientific Workflow

Cloud scientific workflow supplies a collaborative research platform for scholars in different regions in order to implement tasks, such as scientific computing, using various kinds of resources and services provided by cloud computing environment. Aimed to the uncertainty of task completion before a cloud scientific workflow instance starts, a novel task completion prediction model based on data availability/unavailability is proposed in this paper, together with the data availability relationship among them, called the data availability of positive/negative. In this way, the possibility of task completion can be acquired in advance using the data availability/unavailability propagation rules, therefore improves cloud scientific workflow task completion cognition to a large extent. Furthermore, the proposed task completion prediction model has some advantages, such as excellent description and judgement ability. In addition, some experiments have shown that the proposed task completion prediction method can reflect the task accomplishments in practice, and avoid the influence of early task failure to subsequent tasks as far as possible. In other words, it improves the task completion rate while reduces the resource rental expenses in cloud scientific workflow system.