Many techniques such as scheduling and resource provisioning rely on performance prediction of workflow tasks for varying input data. However, such estimates are difficult to generate in the cloud. This paper introduces a novel two-stage machine learning approach for predicting workflow task execution times for varying input data in the cloud. In order to achieve high accuracy predictions, our approach relies on parameters reflecting runtime information and two stages of predictions. Empirical results for four real world workflow applications and several commercial cloud providers demonstrate that our approach outperforms existing prediction methods. In our experiments, our approach respectively achieves a best-case and worst-case estimation error of 1.6 and 12.2 percent, while existing methods achieved errors beyond 20 percent (for some cases even over 50 percent) in more than 75 percent of the evaluated workflow tasks. In addition, we show that the models predicted by our approach for a specific cloud can be ported with low effort to new clouds with low errors by requiring only a small number of executions.

中文翻译：

---

使用两阶段机器学习方法预测云中的工作流任务执行时间

许多技术（例如调度和资源供应）依赖于对不同输入数据的工作流任务的性能预测。然而，这样的估计很难在云中生成。本文介绍了一种新颖的两阶段机器学习方法，用于预测云中不同输入数据的工作流任务执行时间。为了实现高精度预测，我们的方法依赖于反映运行时信息的参数和预测的两个阶段。四个真实世界工作流应用程序和几个商业云提供商的实证结果表明，我们的方法优于现有的预测方法。在我们的实验中，我们的方法分别实现了 1.6% 和 12.2% 的最佳情况和最坏情况估计误差，而现有方法在超过 75% 的评估工作流任务中实现了超过 20%（在某些情况下甚至超过 50%）的错误。此外，我们表明，通过我们的方法为特定云预测的模型只需少量执行即可轻松移植到低错误率的新云中。