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Modeling, classifying and generating large-scale Google-like workload

Da Costa, Georges and Grange, Léo and Courchelle, Inès de Modeling, classifying and generating large-scale Google-like workload. (2018) Sustainable Computing, 19. 305-314. ISSN 2210-5379

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Official URL: https://doi.org/10.1016/j.suscom.2017.12.004

Abstract

One of the key elements needed to test most large-scale scheduling algorithms is a testing infrastructure. Large scale is of upmost importance as failures and complex behaviors are common occurrences only at such scale. In order to test the reaction of a system to failures or extreme behaviors, it is necessary to be able to create large scale environments. Such an infrastructure must be reproducible so that several studies are able to compare themselves but also capable of diversity as otherwise it would risk to limit them to particular subcases. In this article, we propose a generic adaptable and reusable model of large scale workload. The original schema comes from the Google Cluster Workload Traces which is a perfect representative of large scale production workload. The methodology to produce the model can be used at different precision levels and can classify the input level without human supervision. Contrary to most model analysis of such traces, we propose along with our model a reference implementation in order for other studies using our results to produce comparable experiments.

Item Type:Article
HAL Id:hal-02640354
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Funders:
Agence Nationale de la Recherche - ANR (France)
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Deposited On:07 Apr 2020 12:43

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