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              <p>Clusters make use of workload schedulers such as  the Slurm Workload Manager to allocate computing jobs onto  nodes. These schedulers usually aim at a good trade-off between  increasing resource utilization and user satisfaction (decreasing  job waiting time). However, these schedulers are typically unaware  of jobs sharing large input files, which may happen in data  intensive scenarios. The same input files may be loaded several  times, leading to a waste of resources.     We study how to design a data-aware job scheduler that is  able to keep large input files on the computing nodes, without  impacting other memory needs, and can use previously loaded files to  limit data transfers in order to reduce the waiting times of    jobs.  We present three schedulers capable of distributing the load between  the computing nodes as well as re-using an input file already loaded  in the memory of some node as much as possible.    We perform simulations using real cluster usage traces to compare  them to classical job schedulers.  The results show that  keeping data in local memory between successive jobs and using data  locality information to schedule jobs allows a reduction in job  waiting time and a drastic decrease in the amount of data transfers.</p>
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              <p>Les plateformes de calculs utilisent des planificateurs de charges  de travail comme le Slurm Workload Manager pour allouer   des travaux aux nœuds de calcul. Ces ordonnanceurs visent à équilibrer   l'utilisation des ressources et la satisfaction des utilisateurs  (réduire le temps d'attente). Cependant, ces ordonnanceurs ne   sont pas conscients des travaux partageant des fichiers d'entrée volumineux.  Dans certaines situations, la quantité de tels travaux peut être importante.  Le même fichier d'entrée peut être chargé plusieurs fois,  menant à une perte de ressources.    Nous étudions la création d'un ordonnanceur de travaux utilisant la localité  des données capable de garder de larges fichiers d'entrées sur les  nœuds de calculs tout en préservant les autres besoins en mémoire,  et également capable de réutiliser des fichiers déjà chargés afin de limiter  les transferts de données et ainsi réduire le temps d'attente  des travaux.     Nous présentons trois ordonnanceurs capables de distribuer la   charge entre les nœuds de calculs ainsi que de réutiliser   le plus possible  les fichiers d'entrées déjà chargés en mémoire de certains nœuds.    Nous avons effectué des simulations en utilisant l'historique  d'une vraie plateforme de calcul afin de comparer nos algorithmes à   des ordonnanceurs classiques. Les résultats montrent que garder   des données en mémoire entre des travaux successifs et utiliser   la localité des données pour ordonner les travaux permettent une réduction  du temps d'attente des travaux et une diminution drastique de  la quantité de transferts de données.</p>
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