Modeling and Generating large-scale Google-like Workload

Abstract : One of the key element 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 work are able to compare themselves but also capable of diversity as otherwise it would risk to lead 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. 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.
Type de document :
Communication dans un congrès
International Workshop on Resilience and/or Energy-aware techniques for High-Performance Computing , Nov 2016, Hangzhou, China. 2016
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https://hal.laas.fr/hal-01472021
Contributeur : Inès De Courchelle <>
Soumis le : lundi 20 février 2017 - 14:43:22
Dernière modification le : jeudi 6 décembre 2018 - 12:50:06
Document(s) archivé(s) le : dimanche 21 mai 2017 - 14:12:46

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  • HAL Id : hal-01472021, version 1

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Georges Da Costa, Léo Grange, Inès De Courchelle. Modeling and Generating large-scale Google-like Workload. International Workshop on Resilience and/or Energy-aware techniques for High-Performance Computing , Nov 2016, Hangzhou, China. 2016. 〈hal-01472021〉

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