mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
A Profit-aware Allocation of High Performance Computing Applications on Distributed Cloud Data Centers with Environmental Considerations
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1384-5323
University of Tehran, Tehran, Iran.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6132-7945
2014 (English)In: CSI Journal on Computer Science and Engineering JCSE, Vol. 2, no 1, 28-38 p.Article in journal (Refereed) Published
Abstract [en]

A Set of Geographically Distributed Cloud data centers (SGDC) is a promising platform to run a large number of High Performance Computing Applications (HPCAs) in a cost-efficient manner. Energy consumption is a key factor affecting the profit of a cloud provider. In a SGDC, as the data centers are located in different corners of the world, the cost of energy consumption and the amount of CO2 emission significantly vary among the data centers. Therefore, in such systems not only a proper allocation of HPCAs results in CO2 emission reduction, but it also causes a substantial increase of the provider's profit. Furthermore, CO2 emission reduction mitigates the destructive environmental impacts. In this paper, the problem of allocation of a set of HPCAs on a SGDC is discussed where a two-level allocation framework is introduced to deal with the problem. The proposed framework is able to reach a good compromise between CO2 emission and the providers' profit subject to satisfy HPCAs deadlines and memory constraints. Simulation results based on a real intensive workload demonstrate that the proposed framework enhances the CO2 emission by 17% and the provider's profit by 9% in average.

Place, publisher, year, edition, pages
2014. Vol. 2, no 1, 28-38 p.
Keyword [en]
Cloud Computing, Data Center, Energy-aware allocation, CO2 emission, Multi-objective optimization, Live migration.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-35488OAI: oai:DiVA.org:mdh-35488DiVA: diva2:1104172
Projects
PREMISE - Predictable Multicore Systems
Available from: 2017-05-31 Created: 2017-05-31 Last updated: 2017-05-31Bibliographically approved

Open Access in DiVA

No full text

Other links

http://www.jcse.ir/Contents/vol10no24/4.pdf

Search in DiVA

By author/editor
Faragardi, Hamid RezaNolte, thomas
By organisation
Embedded Systems
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

Total: 4 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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