An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics

Qingchen Zhang, Laurence T. Yang*, Zheng Yan, Zhikui Chen, Peng Li

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

105 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3170-3178
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number7
Publication statusPublished - Jul 2018
MoE publication typeA1 Journal article-refereed


  • Canonical polyadic decomposition
  • cloud workload prediction
  • deep learning
  • industry informatics


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