State-based predictions with self-correction on Enterprise Desktop Grid environments |
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Authors: | Josep L Lerida Francesc Solsona Porfidio Hernandez Francesc Gine Mauricio Hanzich Josep Conde |
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Affiliation: | 1. Department of Computer Science, University of Lleida, EPS Building, 25001 Lleida, Spain;2. Department of Mathematics, University of Lleida, EPS Building, 25001 Lleida, Spain;3. Department of Computer Architecture & Operating Systems, Autonomous University of Barcelona, Q Building, 08193 Barcelona, Spain;4. Department of Computer Application in Science and Engineering, Barcelona Supercomputing Center, 08034 Barcelona, Spain |
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Abstract: | 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. |
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Keywords: | System-generated predictions Instance-based learning Application modeling Dynamic prediction correction Enterprise Desktop Grids |
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