https://www.mdu.se/

mdu.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
An Expert System for Managing the Render Farms in Cloud Data Centers
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3548-2973
Gilgamesh Studio, Jordan.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6132-7945
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (CORE)
2024 (English)In: 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 220-225Conference paper, Published paper (Refereed)
Abstract [en]

The users of cloud services prioritize cost andperformance, but they increasingly demand sustainable practices.Sustainability is no longer a choice for businesses but a strategicimperative that shapes global industries. This paper presents anew system for utilizing the render farms in cloud data centers.The system aims to reduce energy consumption and costs in clouddata centers while maintaining a specific level of performance,particularly when rendering images and videos. The system canbe described as a cloud-based expert system that offers renderingas a service, while considering user preferences for performance,cost, and energy efficiency. The system reads different scenerendering parameters and accordingly chooses the most suitableGPUs that fit the user’s requirements. In other words, thesystem inputs are the scene complexity and user preferences.The output is the optimal GPU for rendering. Scene complexityis determined based on several parameters, such as the numberof frames and polygons, resulting in one scene-related value.The user preferences are also normalized to a preferences-relatedvalue. Then, these values are aggregated to determine the optimalavailable GPU to render the scene at the lowest cost, minimumpossible energy consumption, and highest performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. p. 220-225
Keywords [en]
Cloud computing, data centers, Render farms, Rendering-as-a-Service, Performance, Cost, Energy efficiency
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-69629DOI: 10.1109/rtsi61910.2024.10761659ISBN: 9798350362138 (electronic)ISBN: 9798350362145 (print)OAI: oai:DiVA.org:mdh-69629DiVA, id: diva2:1922068
Conference
2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), Milano, Italy, 18-20 September, 2024
Available from: 2024-12-17 Created: 2024-12-17 Last updated: 2024-12-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Al-Dulaimy, AudayNolte, ThomasPapadopoulos, Alessandro V.

Search in DiVA

By author/editor
Al-Dulaimy, AudayNolte, ThomasPapadopoulos, Alessandro V.
By organisation
Embedded Systems
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 16 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