DocumentCode :
3018849
Title :
A Predictive, Decentralized Load Balancing Approach
Author :
Gu, Dazhang ; Yang, Lin ; Welch, Lonnie R.
Author_Institution :
Center for Intelligent, Distributed & Dependable Syst., Ohio Univ., Athens, OH, USA
fYear :
2005
fDate :
04-08 April 2005
Abstract :
The growth of load balancing system raises the issue of scalability, and decentralized load balancing architecture has been proposed to address this issue. In this paper, we investigate how a load balancing architecture can be built on decentralized policies based on CORBA and enhanced by predictive algorithm. The L_2 E predictive filtering model was used to supply workstations with robust cluster load information, which allows them to make more accurate independent allocation decisions. Experimental results showed that our decentralized load balancing approach was able to suppress thrashing and oscillations compared to other load monitoring and prediction techniques, and it was able to achieve a highly balanced system than Sun Grid Engine.
Keywords :
distributed object management; grid computing; resource allocation; workstation clusters; CORBA; Sun Grid Engine; cluster load information; decentralized load balancing architecture; predictive algorithm; predictive filtering model; workstation cluster; Clustering algorithms; Information filtering; Information filters; Load management; Monitoring; Prediction algorithms; Predictive models; Robustness; Scalability; Workstations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Processing Symposium, 2005. Proceedings. 19th IEEE International
Print_ISBN :
0-7695-2312-9
Type :
conf
DOI :
10.1109/IPDPS.2005.60
Filename :
1419968
Link To Document :
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