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
vmBBThrPred: A Black-Box Throughput Predictor for Virtual Machines in Cloud Environments
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
The University of Sydney, Sydney, Australia.
Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0002-9446-8143
2016 (English)In: SERVICE-ORIENTED AND CLOUD COMPUTING, (ESOCC 2016) / [ed] M. Aiello, E.B.Johnsen, S. Dustdar, I. Georgievski, Cham: Springer, 2016, Vol. 9846, p. 18-33Conference paper, Published paper (Refereed)
Abstract [en]

In today's ever computerized society, Cloud Data Centers are packed with numerous online services to promptly respond to users and provide services on demand. In such complex environments, guaranteeing throughput of Virtual Machines (VMs) is crucial to minimize performance degradation for all applications. vmBBThrPred, our novel approach in this work, is an application-oblivious approach to predict performance of virtualized applications based on only basic Hypervisor level metrics. vmBBThrPred is different from other approaches in the literature that usually either inject monitoring codes to VMs or use peripheral devices to directly report their actual throughput. vmBBThrPred, instead, uses sensitivity values of VMs to cloud resources (CPU, Mem, and Disk) to predict their throughput under various working scenarios (free or under contention); sensitivity values are calculated by vmBBProfiler that also uses only Hypervisor level metrics. We used a variety of resource intensive benchmarks to gauge efficiency of our approach in our VMware-vSphere based private cloud. Results proved accuracy of 95% (on average) for predicting throughput of 12 benchmarks over 1200 h of operation.

Place, publisher, year, edition, pages
Cham: Springer, 2016. Vol. 9846, p. 18-33
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743
Keywords [en]
Cloud Computing, Performance Degradation, Performance Modeling
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-45844DOI: 10.1007/978-3-319-44482-6_2ISI: 000388798100002ISBN: 978-3-319-44482-6 (print)OAI: oai:DiVA.org:kau-45844DiVA, id: diva2:968105
Conference
5th IFIP WG 2.14 European Conference on Service-Oriented and Cloud Computing (ESOCC 2016), Vienna, Austria, September 5-7, 2016
Projects
HITsAvailable from: 2016-09-12 Created: 2016-09-12 Last updated: 2019-11-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

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)Centre for HumanIT
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 487 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