State-based predictions with self-correction on Enterprise Desktop Grid environments
Issue date
2013Author
Hernandez, Porfidio
Hanzich, Mauricio
Suggested citation
Lérida Monsó, Josep Lluís;
Solsona Tehàs, Francesc;
Hernandez, Porfidio;
Giné, Francesc;
Hanzich, Mauricio;
Conde Colom, Josep;
.
(2013)
.
State-based predictions with self-correction on Enterprise Desktop Grid environments.
Journal of Parallel and Distributed Computing, 2013, vol. 73, núm. 6, p. 777-789.
https://doi.org/10.1016/j.jpdc.2013.02.007.
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Show full item recordAbstract
The abundant computing resources in current organizations provide new opportunities for executing
parallel scientific applications and using resources. The Enterprise Desktop Grid Computing (EDGC)
paradigm addresses the potential for harvesting the idle computing resources of an organization’s desktop
PCs to support the execution of the company’s large-scale applications. In these environments, the
accuracy of response-time predictions is essential for effective metascheduling that maximizes resource
usage without harming the performance of the parallel and local applications. However, this accuracy is
a major challenge due to the heterogeneity and non-dedicated nature of EDGC resources. In this paper,
two new prediction techniques are presented based on the state of resources. A thorough analysis by
linear regression demonstrated that the proposed techniques capture the real behavior of the parallel
applications better than other common techniques in the literature. Moreover, it is possible to reduce
deviations with a proper modeling of prediction errors, and thus, a Self-adjustable Correction method
(SAC) for detecting and correcting the prediction deviations was proposed with the ability to adapt to the
changes in load conditions. An extensive evaluation in a real environment was conducted to validate the
SAC method. The results show that the use of SAC increases the accuracy of response-time predictions
by 35%. The cost of predictions with self-correction and its accuracy in a real environment was analyzed
using a combination of the proposed techniques. The results demonstrate that the cost of predictions is
negligible and the combined use of the prediction techniques is preferable.