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Virtual machine scheduling based on task characteristic

Published:04 April 2016Publication History

ABSTRACT

Credit scheduler, an original Xen virtual machine scheduler, uses a boost mechanism to improve responsiveness. However, the original boost mechanism is applied only to inactive virtual machines. Because of this, if virtual machine scheduling delay time increases, the I/O performance of an active virtual machine dramatically decreases. However, when the boost mechanism of a previous credit scheduler is applied to an active virtual machine, the allocation equality can become unbalanced. This research study proposes a boost mechanism that can be applied to an active virtual machine. Evaluation of the results showed that, when compared to the previous credit scheduler, the proposed method both did not upset the fairness of the CPU allocation between the virtual machines and improved the I/O performance.

References

  1. Chisnall David, The definitive guide to the xen hypervisor. Pearson Education, 2008 Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Hwanju Kim, Hyeontaek Lim, Jinkyu Jeong, Heeseung Jo amd Joowon Lee, "Task-aware virtual machine scheduling for I/O performance," The 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, 2009 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ryan Hnarakis, In Perfect Xen, "A Performance Study of the Emerging Xen Scheduler", Ph. D, Dissertation, the Faculty of California Polytechnic State University San Luis Obispo, 2013Google ScholarGoogle Scholar
  4. Diego Ongaro, Alan L. Cox and Scott Rixner, "Scheduling I/O in virtual machine monitors," The 2008 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, 2008 Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Virtual machine scheduling based on task characteristic

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    • Published in

      cover image ACM Conferences
      SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing
      April 2016
      2360 pages
      ISBN:9781450337397
      DOI:10.1145/2851613

      Copyright © 2016 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 4 April 2016

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      • research-article

      Acceptance Rates

      SAC '16 Paper Acceptance Rate252of1,047submissions,24%Overall Acceptance Rate1,650of6,669submissions,25%

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