VCPU shaping for supporting latency sensitive workloads

Byung Ki Kim, Hyuck Yoo, Young Woong Ko

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

Abstract

In virtual machine environments, it is difficult to allocate CPU resource in a timely manner when lots of domains are in BOOST priority. In this paper, we present a virtual machine scheduling scheme based on VCPU shaping and efficiently deals with multi BOOST problem. We evaluate our prototype in terms of latency over diverse workloads. Our experiment result shows that the proposed realtime priority scheme effectively allocates CPU resources for low latency guest domain over varying workloads.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages25-32
Number of pages8
Volume7709 LNCS
DOIs
Publication statusPublished - 2012 Dec 14
Event4th International Conference on Future Generation Information Technology, FGIT 2012 - Gangneug, Korea, Republic of
Duration: 2012 Dec 162012 Dec 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7709 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Conference on Future Generation Information Technology, FGIT 2012
CountryKorea, Republic of
CityGangneug
Period12/12/1612/12/19

Fingerprint

Virtual Machine
Program processors
Workload
Latency
Machine Scheduling
Resources
Scheduling
Prototype
Real-time
Evaluate
Experiment
Experiments
Virtual machine

Keywords

  • QoS
  • realtime
  • resource monitor
  • scheduler
  • Xen

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kim, B. K., Yoo, H., & Ko, Y. W. (2012). VCPU shaping for supporting latency sensitive workloads. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7709 LNCS, pp. 25-32). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7709 LNCS). https://doi.org/10.1007/978-3-642-35585-1_4

VCPU shaping for supporting latency sensitive workloads. / Kim, Byung Ki; Yoo, Hyuck; Ko, Young Woong.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7709 LNCS 2012. p. 25-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7709 LNCS).

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

Kim, BK, Yoo, H & Ko, YW 2012, VCPU shaping for supporting latency sensitive workloads. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7709 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7709 LNCS, pp. 25-32, 4th International Conference on Future Generation Information Technology, FGIT 2012, Gangneug, Korea, Republic of, 12/12/16. https://doi.org/10.1007/978-3-642-35585-1_4
Kim BK, Yoo H, Ko YW. VCPU shaping for supporting latency sensitive workloads. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7709 LNCS. 2012. p. 25-32. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-35585-1_4
Kim, Byung Ki ; Yoo, Hyuck ; Ko, Young Woong. / VCPU shaping for supporting latency sensitive workloads. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7709 LNCS 2012. pp. 25-32 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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