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Adaptive sliding windows for improved estimation of data center resource utilization

Author
Baig, S.; Iqbal, W.; Berral, J.; Carrera, D.
Type of activity
Journal article
Journal
Future generation computer systems
Date of publication
2020-03
Volume
104
First page
212
Last page
224
DOI
10.1016/j.future.2019.10.026
Project funding
HiEST: Holistic Integration of Emerging Supercomputing Technologies
High performance computing VII
Models de Programacio i Entorns d'eXecució PARal.lels
Repository
http://hdl.handle.net/2117/186459 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0167739X19309203 Open in new window
Abstract
Accurate prediction of data center resource utilization is required for capacity planning, job scheduling, energy saving, workload placement, and load balancing to utilize the resources efficiently. However, accurately predicting those resources is challenging due to dynamic workloads, heterogeneous infrastructures, and multi-tenant co-hosted applications. Existing prediction methods use fixed size observation windows which cannot produce accurate results because of not being adaptively adjusted...
Citation
Baig, S. [et al.]. Adaptive sliding windows for improved estimation of data center resource utilization. "Future generation computer systems", Març 2020, vol. 104, p. 212-224.
Keywords
Adaptive observation window, Data center, Resource estimation, Sliding windows, Time series
Group of research
CAP - High Performace Computing Group

Participants

  • Baig, Shuja-ur-rehman  (author)
  • Iqbal, Waheed  (author)
  • Berral Garcia, Josep Lluis  (author)
  • Carrera Perez, David  (author)

Attachments