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Forecasting Duration Intervals of Scientific Workflow Activities Based on Time-Series Patterns

机译:基于时间序列模式的科学工作流活动的持续时间间隔

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In scientific workflow systems, time related functionalities such as workflow scheduling and temporal verification normally require effective forecasting of activity durations due to the dynamic nature of underlying resources such as Web or Grid services. However, most existing strategies cannot handle well the problems of limited sample size and frequent turning points which are typical for the duration series of scientific workflow activities. To address such problems, we propose a novel pattern based time-series forecasting strategy which utilises a periodical sampling plan to build representative duration series, and then conducts time-series segmentation to discover the smallest pattern set and predicts the activity duration intervals with pattern matching results. The simulation experiment demonstrates the excellent performance of our segmentation algorithm and further shows the effectiveness of our strategy in the prediction of activity duration intervals, especially the ability of handling turning points.
机译:在科学工作流系统中,由于Web或网格服务等底层资源的动态性质,工作流程调度和时间验证等时间相关功能通常需要有效预测活动持续性。然而,大多数现有策略无法处理有限的样本大小和频繁转位的问题,这对于持续时间系列的科学工作流程活动是典型的。为了解决此类问题,我们提出了一种基于模式的时间序列预测策略,该策略利用期刊采样计划来构建代表性持续时间序列,然后进行时间序列分割以发现最小的模式集并预测模式匹配的活动持续时间间隔结果。仿真实验表明了我们的分割算法的优异性能,进一步示出了我们对活动持续时间间隔预测的策略的有效性,尤其是处理转折点的能力。

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