|
Título:
|
State-based predictions with self-correction on Enterprise Desktop Grid environments
|
|
Autor/a:
|
Lérida Monsó, Josep Lluís; Solsona Tehàs, Francesc; Hernandez, Porfidio; Giné, Francesc; Hanzich, Mauricio; Conde Colom, Josep
|
|
Notas:
|
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.
This work was supported by the Spanish Ministry of Science and Technology under grant No. TIN2011-28689-C02-02 and No. TIN2010-18978. |
|
Materia(s):
|
-System-generated predictions
-Instance-based learning
-Application modeling |
|
Derechos:
|
(c) Elsevier, 2013
info:eu-repo/semantics/restrictedAccess |
|
Tipo de documento:
|
article
publishedVersion |
|
Editor:
|
Elsevier
|
|
Compartir:
|
|
