TY  - GEN
SP  - 1
SN  - 1424409101
Y1  - 2007///
AV  - public
UR  - http://dx.doi.org/10.1109/IPDPS.2007.370306
N1  - ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

21st International Parallel and Distributed Processing Symposium (IPDPS 2007): Proceedings, 26-30 March 2007, Long Beach, California, USA.
N2  - The integration of clusters of computers into computational grids has recently gained the attention of many computational scientists. While considerable progress has been made in building middleware and workflow tools that facilitate the sharing of compute resources, little attention has been paid to grid scheduling and load balancing techniques to reduce job waiting time. Based on a detailed analysis of usage characteristics of an existing grid that involves a large CPU cluster, we observe that grid scheduling decisions can be significantly improved if the characteristics of current usage patterns are understood and extrapolated into the future. The paper describes an architecture and an implementation for a predictive grid scheduling framework which relies on Kalman filter theory to predict future CPU resource utilisation. By way of replicated experiments we demonstrate that the prediction achieves a precision within 15-20% of the utilisation later observed and can significantly improve scheduling quality, compared to approaches that only take into account current load indicators.
PB  - IEEE Computer Society Press
ID  - discovery5617
EP  - 10
A1  - Chapman, C.
A1  - Musolesi, M.
A1  - Emmerich, W.
A1  - Mascolo, C.
TI  - Predictive resource scheduling in computational grids
ER  -