Skip to main content
placeholder image

Using neural networks to forecast available system resources: an approach and empirical investigation

Journal Article


Abstract


  • Software aging refers to the phenomenon that software systems show progressive performance degradation or a sudden crash after longtime execution. It has been reported that this phenomenon is closely related to the exhaustion of system resources. This paper quantitatively studies available system resources under the real-world situation where workload changes dynamically over time. We propose a neural network approach to first investigate the relationship between available system resources and system workload and then to forecast future available system resources. Experimental results on data sets collected from real-world computer systems demonstrate that the proposed approach is effective.

Authors


  •   Jia, Yun-Fei (external author)
  •   Zhi Quan (George) Zhou
  •   Xue, Ke-Xian (external author)
  •   Zhao, Lei (external author)
  •   Cai, Kai-Yuan (external author)

Publication Date


  • 2015

Citation


  • Jia, Y., Zhou, Z. Quan., Xue, K., Zhao, L. & Cai, K. (2015). Using neural networks to forecast available system resources: an approach and empirical investigation. International Journal of Software Engineering and Knowledge Engineering, 25 (4), 781-802.

Scopus Eid


  • 2-s2.0-84941884211

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5211

Has Global Citation Frequency


Number Of Pages


  • 21

Start Page


  • 781

End Page


  • 802

Volume


  • 25

Issue


  • 4

Place Of Publication


  • Singapore

Abstract


  • Software aging refers to the phenomenon that software systems show progressive performance degradation or a sudden crash after longtime execution. It has been reported that this phenomenon is closely related to the exhaustion of system resources. This paper quantitatively studies available system resources under the real-world situation where workload changes dynamically over time. We propose a neural network approach to first investigate the relationship between available system resources and system workload and then to forecast future available system resources. Experimental results on data sets collected from real-world computer systems demonstrate that the proposed approach is effective.

Authors


  •   Jia, Yun-Fei (external author)
  •   Zhi Quan (George) Zhou
  •   Xue, Ke-Xian (external author)
  •   Zhao, Lei (external author)
  •   Cai, Kai-Yuan (external author)

Publication Date


  • 2015

Citation


  • Jia, Y., Zhou, Z. Quan., Xue, K., Zhao, L. & Cai, K. (2015). Using neural networks to forecast available system resources: an approach and empirical investigation. International Journal of Software Engineering and Knowledge Engineering, 25 (4), 781-802.

Scopus Eid


  • 2-s2.0-84941884211

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5211

Has Global Citation Frequency


Number Of Pages


  • 21

Start Page


  • 781

End Page


  • 802

Volume


  • 25

Issue


  • 4

Place Of Publication


  • Singapore