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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/115762...
Part of book or chapter of book . 2005 . Peer-reviewed
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DBLP
Conference object . 2017
Data sources: DBLP
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A Chronological History-Based Execution Time Estimation Model for Embarrassingly Parallel Applications on Grids

Authors: Chao-Tung Yang; Po-Chi Shih; Cheng-Fang Lin; Ching-Hsien Hsu; Kuan-Ching Li;

A Chronological History-Based Execution Time Estimation Model for Embarrassingly Parallel Applications on Grids

Abstract

In order to identify and schedule jobs that are suitable for determined resources, an execution time estimation model is required. In this paper, it is described a Chronological history-based execution time estimation model to predict current execution time, according to the previous execution results. We built a heterogeneous computational Grid environment using Globus Toolkit, and our research is focused in Grid computing environments and to execute parallel jobs on multiple resources by measuring its accuracy. The experimental results shown that our model can accurately predict the execution time of embarrassingly parallel applications.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Average
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