Time-Dependent Cloud Workload Forecasting via Multi-Task Learning

Jing Bi, Haitao Yuan, Meng Chu Zhou, Qing Liu

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Cloud services have rapidly grown in cloud data centers (CDCs). Accurate workload prediction benefits CDCs since appropriate resource provisioning can be performed for their providers to ensure the full satisfaction of service-level agreement (SLA) requirements from users. Yet these providers face some challenging issues in accurate workload prediction, i.e., how to achieve high accuracy and fast learning of prediction models. Consistent efforts have been made to address them. This letter proposes an innovative integrated forecasting method that combines stochastic configuration networks with Savitzky-Golay smoothing filter and wavelet decomposition to forecast workload at the succeeding time slot. We first smooth the workload via a Savitzky-Golay filter. Then, we adopt wavelet decomposition to decompose smoothed outcome into multiple components. Supported by stochastic configuration networks, an integrated model is established, which can well describe statistical features both of detail and trend components. Extensive experimental outcomes have explicated that our approach realizes better prediction results and quicker training than those of representative prediction approaches.

Original languageEnglish (US)
Article number8641310
Pages (from-to)2401-2406
Number of pages6
JournalIEEE Robotics and Automation Letters
Issue number3
StatePublished - Jul 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


  • Cloud data centers
  • Savitzky-Golay filter
  • Stochastic configuration networks (SCNs)
  • Wavelet decomposition
  • Workload forecasting


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