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À. Une-telle-Échelle, Au niveau de l'application, le matériel est réparti physiquement entre différents utilisateurs De ce fait, les ressources allouées à une application ne garantissent pas de fournir des performances constantes pendant la durée de vie de cette application Cette hétérogénéité, à son tour, aboutit à une évidente variabilité de performance [131]. D'autre part, les infrastructures à grande échelle se composent de milliers de machines qui consomment collectivement une énorme quantité d'énergie, ce qui entraîne un énorme coût opérationnel [46]. Par exemple, la consommation annuelle d'électricité des datacenters de Google dépasse 1.120 GWh, ce qui correspond à une facture d'électricité de 67 M $ [92]. À l'avenir, la variabilité de performance et la consommation d'énergie continueront d'être des préoccupations majeures pour la conception et l'exploitation des systèmes de traitement Big Data [74, 97]. L'échelle des infrastructures sous-jacentes doit augmenter pour faire face à l'augmentation implacable de la taille des données. Cette échelle croissante augmentera non seulement la variabilité de performance mais aussi la consommation d'énergie . À titre indicatif, les besoins en énergie pour le fonctionnement des systèmes de traitement Big Data devraient atteindre l'equivalent de la production d'une centrale nucléaire moyenne [82]. Dans le contexte du Big Data, un calcul se compose généralement d'un très grand nombre de tâches élémentaires. La performance d'un calcul est déterminée par la fin de sa dernière tâche. En raison de la variabilité élevée de performance, les temps d'exécution des tâches peuvent varier de manière importante au sein du même calcul. Même si les temps d'exécution d'un grand nombre de tâches restent proches du temps d'exécution moyen, certains d'entre eux peuvent présenter une très grande déviation. Il n'est pas rare dans la pratique d'observer certaines tâches avec des temps d'exécution jusqu'à huit fois plus longs que le temps d'exécution moyen, hétérogénéité des ressources est inévitable. Elle apparaît à différents niveaux des systèmes. Au niveau du matériel, plusieurs générations de matériel coexistent dans les infrastructures des clouds. Par conséquent, les utilisateurs n'ont aucun contrôle sur les matériels qui leur sont attribués Ce phénomène est appelé distribution heavy-tail [94]. Il a un impact négatif sur la performance du calcul [131]. Dans le domaine du Big Data, ces tâches nuisibles sont appelées stragglers. Il existe un grand nombre de travaux consacrés à la réduction de la fréquence d'apparition de stragglers Cependant, la variabilité de performance entraine l'apparition de stragglers inattendus. Dans la pratique, il a été démontré que ces stragglers ont un impact majeur sur la performance [131]. En conséquence, la prévention des stragglers est un objectif crucial pour améliorer les performances des grands systèmes de traitement Big Data, p.41