Deep learning, as the most important architecture of current computational intelligence, achieves super performance to predict the cloud workload for industry informatics. However, it is a nontrivial task to train a deep learning model efficiently since the deep learning model often includes a great number of parameters. In this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics. In the proposed model, the parameters are compressed significantly by converting the weight matrices to the canonical polyadic format. Furthermore, an efficient learning algorithm is designed to train the parameters. Finally, the proposed efficient deep learning model is applied to the workload prediction of virtual machines on cloud. Experiments are conducted on the datasets collected from PlanetLab to validate the performance of the proposed model by comparing with other machine-learning-based approaches for workload prediction of virtual machines. Results indicate that the proposed model achieves a higher training efficiency and workload prediction accuracy than state-of-the-art machine-learning-based approaches, proving the potential of the proposed model to provide predictive services for industry informatics.

中文翻译：

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用于预测行业信息学云工作负载的高效深度学习模型


深度学习作为当前计算智能最重要的架构，具有超强的性能来预测行业信息学的云工作负载。然而，有效训练深度学习模型并不是一项简单的任务，因为深度学习模型通常包含大量参数。本文提出了一种基于规范多元分解的高效深度学习模型来预测行业信息学的云工作负载。在所提出的模型中，通过将权重矩阵转换为规范多元格式来显着压缩参数。此外，设计了一种有效的学习算法来训练参数。最后，将所提出的高效深度学习模型应用于云上虚拟机的工作负载预测。对从 PlanetLab 收集的数据集进行了实验，通过与其他基于机器学习的虚拟机工作负载预测方法进行比较来验证所提出模型的性能。结果表明，与最先进的基于机器学习的方法相比，所提出的模型实现了更高的训练效率和工作负载预测准确性，证明了所提出的模型为行业信息学提供预测服务的潜力。