Adaptive virtual resource management with fuzzy model predictive control

Lixi Wang, Jing Xu, Ming Zhao, José Fortes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Resource management in virtualized systems remains a key challenge where the applications have dynamically changing workloads and the virtual machines (VMs) compete for the shared resources in a convolved manner. To address this challenge, this paper proposes a new resource management approach based on Fuzzy Model Predictive Control (FMPC) which can effectively capture the nonlinear behaviors in VM resource usages through fuzzy modeling and quickly adapt to the changes in the virtualized system through predictive control. This approach is capable of optimizing the VM-to-resource allocations according to high-level service differentiation or revenue maximization objectives. A prototype of this approach was implemented for Xen-based VM systems and evaluated using a typical online transaction benchmark (RUBiS). The results demonstrate that the proposed approach can efficiently allocate CPU resource to multiple VMs to achieve application- or system-level performance objective.

Original languageEnglish (US)
Title of host publicationProceedings of the 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops
Pages191-192
Number of pages2
DOIs
StatePublished - 2011
Externally publishedYes
Event8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops - Karlsruhe, Germany
Duration: Jun 14 2011Jun 18 2011

Other

Other8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops
CountryGermany
CityKarlsruhe
Period6/14/116/18/11

Fingerprint

Model predictive control
Model Predictive Control
Virtual Machine
Resource Management
Fuzzy Model
Resources
Service Differentiation
Fuzzy Modeling
Predictive Control
Resource Allocation
Resource allocation
Program processors
Transactions
Workload
Virtual machine
Prototype
Benchmark
Demonstrate

Keywords

  • fuzzy modeling
  • predictive control
  • resource management
  • virtualization

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Wang, L., Xu, J., Zhao, M., & Fortes, J. (2011). Adaptive virtual resource management with fuzzy model predictive control. In Proceedings of the 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops (pp. 191-192) https://doi.org/10.1145/1998582.1998623

Adaptive virtual resource management with fuzzy model predictive control. / Wang, Lixi; Xu, Jing; Zhao, Ming; Fortes, José.

Proceedings of the 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops. 2011. p. 191-192.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, L, Xu, J, Zhao, M & Fortes, J 2011, Adaptive virtual resource management with fuzzy model predictive control. in Proceedings of the 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops. pp. 191-192, 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops, Karlsruhe, Germany, 6/14/11. https://doi.org/10.1145/1998582.1998623
Wang L, Xu J, Zhao M, Fortes J. Adaptive virtual resource management with fuzzy model predictive control. In Proceedings of the 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops. 2011. p. 191-192 https://doi.org/10.1145/1998582.1998623
Wang, Lixi ; Xu, Jing ; Zhao, Ming ; Fortes, José. / Adaptive virtual resource management with fuzzy model predictive control. Proceedings of the 8th ACM International Conference on Autonomic Computing, ICAC 2011 and Co-located Workshops. 2011. pp. 191-192
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