Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
vmBBProfiler: A BlackBox Profiling Approach to Quantify Sensitivity of Virtual Machines to Shared Cloud Resources
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Computer Networking, DISCO)ORCID iD: 0000-0001-9194-010X
University of Sydney, Australia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Computer Networking, DISCO)ORCID iD: 0000-0002-9446-8143
2017 (English)In: Computing, ISSN 0010-485X, E-ISSN 1436-5057, Vol. 99, no 12, p. 1149-1177Article in journal (Refereed) Published
Abstract [en]

Virtualized Data Centers are packed with numerous web and cloud servicesnowadays. In such large infrastructures, providing reliable service platforms dependsheavily on efficient sharing of physical machines (PMs) by virtual machines (VMs).To achieve efficient consolidation, performance degradation of co-located VMs mustbe correctly understood, modeled, and predicted. This work is a major step towardunderstanding such baffling phenomena by not only identifying, but also quantifyingsensitivity of general purpose VMs to their demanded resources. vmBBProfiler, ourproposed system in this work, is able to systematically profile behavior of any generalpurpose VM and calculate its sensitivity to system provided resources such as CPU,Memory, and Disk. vmBBProfiler is evaluated using 12 well-known benchmarks,varying from pure CPU/Mem/Disk VMs to mixtures of them, on three different PMsin our VMware-vSphere based private cloud. Extensive empirical results conductedover 1200h of profiling prove the efficiency of our proposed models and solutions; italso opens doors for further research in this area. vmBBProfiler: a black-box profiling approach to quantify sensitivity of virtual machines to shared cloud resources (PDF Download Available).

Place, publisher, year, edition, pages
Springer, 2017. Vol. 99, no 12, p. 1149-1177
Keywords [en]
Performance degradation, Virtualization, Cloud computing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-64566DOI: 10.1007/s00607-017-0552-yISI: 000414347900001OAI: oai:DiVA.org:kau-64566DiVA, id: diva2:1155760
Projects
HITSAvailable from: 2017-11-09 Created: 2017-11-09 Last updated: 2022-06-29Bibliographically approved

Open Access in DiVA

fulltext(3677 kB)257 downloads
File information
File name FULLTEXT01.pdfFile size 3677 kBChecksum SHA-512
b353f2202d7e97602cf23e75440594caeae6f3931b12b5747c58c28f78b3e01e7fb7802a34640c323793ea2681fa6cb10ab1bf7a7f9a1539d9966b138744d093
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Taheri, JavidKassler, Andreas

Search in DiVA

By author/editor
Taheri, JavidKassler, Andreas
By organisation
Department of Mathematics and Computer Science (from 2013)
In the same journal
Computing
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 257 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 439 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf