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首页> 外文期刊>International Journal on Informatics Visualization: JOIV >Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques
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Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques

机译：使用机器学习技术的基于模型的资源利用和性能风险预测

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摘要

The growing complexity of modern software systems makes the performance prediction a challenging activity. Many drawbacks incurred by using the traditional performance prediction techniques such as time consuming and inability to surround all software system when large scaled. To contribute to solving these problems, we adopt a model-based approach for resource utilization and performance risk prediction. Firstly, we model the software system into annotated UML diagrams. Secondly, performance model is derived from UML diagrams in order to be evaluated. Thirdly, we generate performance and resource utilization training dataset by changing workload. Finally, when new instances are applied we can predict resource utilization and performance risk by using machine learning techniques. The approach will be used to enhance work of human experts and improve efficiency of software system performance prediction. In this paper, we illustrate the approach on a case study. A performance training dataset has been generated, and three machine learning techniques are applied to predict resource utilization and performance risk level. Our approach shows prediction accuracy within 68.9 % to 93.1 %.
机译：现代软件系统日益复杂，使得性能预测成为一项具有挑战性的活动。使用传统的性能预测技术会带来许多缺点，例如耗时且无法大规模扩展所有软件系统。为了解决这些问题，我们采用了基于模型的方法来进行资源利用和性能风险预测。首先，我们将软件系统建模为带注释的UML图。其次，性能模型是从UML图导出的，以便进行评估。第三，我们通过更改工作量来生成性能和资源利用培训数据集。最后，当应用新实例时，我们可以使用机器学习技术来预测资源利用率和性能风险。该方法将用于增强人类专家的工作并提高软件系统性能预测的效率。在本文中，我们将通过案例研究说明该方法。已经生成了性能训练数据集，并且应用了三种机器学习技术来预测资源利用率和性能风险级别。我们的方法显示出68.9％至93.1％的预测准确性。

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