[en] Energy efficiency and high performance computing are the basic design consider- ations across modern-day computing solutions due to different concerns, such as system functioning, operational cost, and environmental issues. Opportunistic grid infrastructures offer computational power at low cost focused on harvesting idle computing cycles of existing commodity computing resources. Other than allow- ing the customization of execution environments, virtualization is considered as one key technique to reduce energy consumption in large-scale systems and contributes to the scalability of the system. This work presented an energy efficient approach for opportunistic grids based on virtualization. The experimental results showed that depending on the strategy used to deploy virtual machines on desktop machines, virtu- alization significantly improves the energy efficiency of opportunistic grids compared with dedicated computing systems, without disturbing the owner-user.
Disciplines :
Computer science
Author, co-author :
Castro, Harold; Universidad de los Andes - Uniandes > Department of System and Computing Engineering > Head > PhD
Villamizar, Mario; Universidad de los Andes - Uniandes > Department of System and Computing Engineering > Msc.
Sotelo, German; Universidad de los Andes - Uniandes > Department of System and Computing Engineering > Msc.
DIAZ, Cesar ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
PECERO, Johnatan ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
BOUVRY, Pascal ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Khan, Samee; North Dakota State University > Electrical and Computer Engineering > PhD
Language :
English
Title :
GFOG: Green and Flexible Opportunistic Grids.
Publication date :
January 2013
Main work title :
Scalable Computing and Communications. Theory and Practice