Feb 23, 2018 · In this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry ...
An efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics and ...
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 this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics ...
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Experimental results on real-world dataset have shown that the proposed CNN-LSTM model is effective on VM workload prediction, and when compared to ...
An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics. In: IEEE Transactions on Industrial. Informatics, 14(7), pp. 3170–3178 ...
An efficient supervised learning-based Deep Neural Network (esDNN) algorithm is proposed for cloud workload prediction to learn and capture the features of ...
In this paper, we present LoadDynamics, a generic workload prediction framework that can generate highly-accurate work- load predictors for various dynamic ...
Workload prediction using Deep Learning (DL) is a popular method of inferring complicated multidimensional data of cloud environments to meet this requirement.
An efficient supervised learning-based Deep Neural Network (esDNN) algorithm is proposed for cloud workload prediction to learn and capture the features of ...
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