A multilayer perceptron–artificial neural network (MLP-ANN)-based prediction model is proposed to predict the execution time of tasks in cloud environment. Significant input parameters are identified and selected through interpretive structural modeling (ISM) approach. A prediction model is proposed for predicting the task execution time for varying number of inputs. The proposed model is validated and provides 21.7% reduction in mean relative error compared to other state-of-the-art methods.

中文翻译：

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基于MLP-ANN的执行时间预测模型和通过结构建模评估输入参数

提出了一种基于多层感知器－人工神经网络（MLP-ANN）的预测模型，以预测云环境中任务的执行时间。通过解释性结构建模（ISM）方法来识别和选择重要的输入参数。提出了一种预测模型，用于预测不同数量输入的任务执行时间。与其他最新方法相比，所提出的模型已经过验证，并且平均相对误差降低了21.7％。