umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Peer to peer resource management for cloud data centers
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
(English)Manuscript (preprint) (Other academic)
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-87480OAI: oai:DiVA.org:umu-87480DiVA: diva2:709527
Available from: 2014-04-02 Created: 2014-04-02 Last updated: 2014-04-03Bibliographically approved
In thesis
1. Capacity Management Approaches for Compute Clouds
Open this publication in new window or tab >>Capacity Management Approaches for Compute Clouds
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Cloud computing provides the illusion of a seamless, infinite resource pool with flexibleon-demand accessibility. However, behind this illusion there are thousands ofservers and peta-bytes of storage, running tens of thousands of applications accessedby millions of users. The management of such systems is non-trivial because theyface elastic demand, have heterogeneous resources, must fulfill diverse managementobjectives, and are vast in scale.Autonomic computing techniques can be used to tackle the complex problem ofresource management in cloud data centers by introducing self-managing elementsknown as autonomic managers. Each autonomic manager should be capable of managingitself while simultaneously contributing to the fulfillment of high level systemwideobjectives. A wide range of approaches and mechanisms can be used to defineand design these autonomic managers as well as to organize them and coordinate theiractions in order to achieve specific goals.This thesis investigates autonomic approaches for cloud resource management thataim to optimize the cloud infrastructure layer with respect to various high level objectives.The resource management problem is formulated as a problem of optimizationwith respect to one or more management objectives such as cost, profitability, or datacenter utilization, as well as performance concerns such as response time, quality ofservice, and rejection rates. The aim of the reported investigations is to address theproblems of cost-efficient elastic resource provisioning, unified management of cloudresources, and scalability in cloud resource management. This is achieved by introducingthree new concepts in capacity management: the Repacking, Holistic, and Peerto Peer approaches.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2013. 66 p.
Series
Report / UMINF, ISSN 0348-0542 ; 2013:24
Keyword
Cloud computing, Capacity Management
National Category
Computer Science
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-87242 (URN)978-91-7459-788-2 (ISBN)
Presentation
2013-12-19, Umeå universitet, Umeå, 10:00
Opponent
Supervisors
Available from: 2014-04-03 Created: 2014-03-25 Last updated: 2014-04-03Bibliographically approved

Open Access in DiVA

No full text

Authority records BETA

Sedaghat, MinaHernandez, FranciscoElmroth, Erik

Search in DiVA

By author/editor
Sedaghat, MinaHernandez, FranciscoElmroth, Erik
By organisation
Department of Computing Science
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 212 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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