Enhancing Long-Term Cloud Workload Forecasting Framework: Anomaly Handling and Ensemble Learning in Multivariate Time Series

Yeong Min Kim, Seunghwan Song, Byoung Mo Koo, Jeena Son, Yeseul Lee, Jun Geol Baek

Research output: Contribution to journalArticlepeer-review


Forecasting workloads and responding promptly with resource scaling and migration is critical to optimizing operations and enhancing resource management in cloud environments. However, the diverse and dynamic nature of devices within cloud environments complicates workload forecasting. These challenges often lead to service level agreement violations or inefficient resource usage. Hence, this paper proposes an Enhanced Long-Term Cloud Workload Forecasting (E-LCWF) framework designed specifically for efficient resource management in these heterogeneous and dynamic environments. The E-LCWF framework processes individual resource workloads as multivariate time series and enhances model performance through anomaly detection and handling. Additionally, the E-LCWF framework employs an error-based ensemble approach, using transformer-based models and Long-Term Time Series Forecasting (LTSF) linear models, each of which has demonstrated exceptional performance in LTSF. Experimental results obtained using virtual machine data from real-world management information systems and manufacturing execution systems show that the E-LCWF framework outperforms state-of-the-art models in forecasting accuracy.

Original languageEnglish
Pages (from-to)789-799
Number of pages11
JournalIEEE Transactions on Cloud Computing
Issue number2
Publication statusPublished - 2024 Apr 1

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Anomaly detection
  • ensemble learning
  • long-term cloud workload forecasting
  • multivariate time series analysis
  • resource management

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications


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