Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Electricity Price-aware Consolidation Algorithms for Time-sensitive VM Services in Cloud Systems
School of Computer Science, Southeast University, Nanjing, Jiangsu, China.
School of Computer Science and Engeering, Southeast University, Nanjing, Jiangsu China.
Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang China.
School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu China.
Visa övriga samt affilieringar
2019 (Engelska)Ingår i: IEEE Transactions on Services Computing, ISSN 1939-1374, E-ISSN 1939-1374Artikel i tidskrift (Refereegranskat) Epub ahead of print
Abstract [en]

Despite the salient feature of cloud computing, the cloud provider still suffers from electricity bill, which mainly comes from 1) the power consumption of running physical machines and 2) the dynamically varying electricity price offered by smart grids. In the literature, there exist viable solutions adaptive to electricity price variation to reduce the electricity bill. However, they are not applicable to serving time-sensitive VM requests. In serving time-sensitive VM requests, it is potential for the cloud provider to apply proper consolidation strategies to further reduce the electricity bill. In this work, to address this challenge, we develop electricity-price-aware consolidation algorithms for both the offline and online scenarios. For the offline scenario, we first develop an consolidation algorithm with constant approximation, which always approaches the optimal solution within a constant factor of 5. For the online scenario, we propose an $O(\log(\frac{L_{max}}{L_{min}}))$ -competitive algorithm that is able to approach the optimal offline solution within a logarithmic factor, where $\frac{L_{max}}{L_{min}}$ is the ratio of the longest length of the processing time requirement of VMs to the shortest one. Our trace-driven simulation results further demonstrate that the average performance of the proposed algorithms produce near-optimal electricity bill.

Ort, förlag, år, upplaga, sidor
IEEE, 2019.
Nyckelord [en]
Cloud computing, Approximation algorithms, Data centers, Resource management, Quality of service, Power demand, Virtual machining
Nationell ämneskategori
Medieteknik
Forskningsämne
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-73551DOI: 10.1109/TSC.2019.2894742OAI: oai:DiVA.org:ltu-73551DiVA, id: diva2:1303724
Tillgänglig från: 2019-04-10 Skapad: 2019-04-10 Senast uppdaterad: 2019-04-10

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Personposter BETA

Vasilakos, Athanasios

Sök vidare i DiVA

Av författaren/redaktören
Vasilakos, Athanasios
Av organisationen
Datavetenskap
I samma tidskrift
IEEE Transactions on Services Computing
Medieteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 1 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf