Reference : Learning Reliability Models of Grid Resource Supplying
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/1292
Learning Reliability Models of Grid Resource Supplying
English
Briquet, Cyril mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Informatique (ingénierie du logiciel et algorithmique) >]
de Marneffe, Pierre-Arnoul mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Informatique (ingénierie du logiciel et algorithmique) >]
22-Nov-2005
CGW'05 Proceedings
Bubak, Marian
Turala, Michal
Wiatr, Kazimierz
Yes
No
International
Cracow Grid Workshop
du 20 novembre 2005 au 23 novembre 2005
ACC Cyfronet AGH
Cracow
Poland
[en] Grid computing ; Machine Learning
[en] Resource exchange between Grid participants is at the core of Grid computing. Distributed bartering is a distributed and moneyless method of resource exchange. Recent work related to distributed bartering has mainly dealt with resource supplying. However, Grid participants still face an unstable resource environment due to the partial and intermittent nature of the exchanged resources. The problem considered in this paper is the unreliability of resource supplying. Though it cannot be totally avoided, a proactive stance may lower its impact in the long run. We propose to explore the reduction of performance variability by improving resource consumption. The goal is to enable Grid participants to identify and avoid unreliable resource suppliers by learning reliability models of resource supplying. A Machine Learning problem is defined and the generated models are applied to select more reliable resources in the hope of improving resource consumption.
Researchers
http://hdl.handle.net/2268/1292

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
learning_reliability_models_2005.pdfAuthor postprint122.52 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.