, Amélioration de l'allocation des ressources aux VMsà partir de prédictions de leur durée de vie

. .. Etat, 2.1 Caractérisation de la charge sur les plateformes
URL : https://hal.archives-ouvertes.fr/in2p3-00658478

C. .. De-l'utilisation-de-la-plateforme-d'outscale, 3.2 Utilisation des ressources matérielles et interférences entre les VMs

R. .. , 121 7.5.1 Présentation des algorithmes de placement des VMs en ligne

.. .. Conclusion-générale, , p.127

. .. Conclusion, 106 6.1.1 Identified Opportunities Regarding the Management of Resources

, profitabilité de l'infrastructure en ajustant les prix des VMsà la demande, et en n'exécutant que les plus rentables, vol.45

. Enfin, certains travaux visentà respecter les accords de services passés sur la QoS fournie

, La définition d'une fonction objectif requiert de modéliser l'utilisation de ressources et la QoS des VMs. Certains modèles considèrent que la QoS est acceptable tant que l'utilisation d'un serveur ne dépasse pas un seuil

, Ces modèles ont deux limitations. Premièrement, l'utilisation de ressources micro-architecturales

, Ensuite, la tolérance aux interférences des applications qui s'exécutent sur les VMs est variable, vol.63

, C'est pourquoi certains travaux s'appuient sur des mesures d'interférences collectées pour différentes configurations de co-localisation des applications

. Cependant, ces modèles de performance ne peuvent pour l'instant pasêtreétablis dans un cloud IaaS, car le fournisseur n'a pas connaissance de l'identité de l'application qui s'exécute dans une VM

, Une fois que la fonction objectif et le modèle d'utilisation des ressources ontété choisis, il faut déterminer comment explorer au mieux l'ensemble des solutions acceptables. La difficulté provient du fait que le problème est NP-complet, l'ensemble de solutions croît exponentiellement avec le nombre de serveurs, et l'on ne sait pas si il existe une méthode qui garantisse de trouver la solution optimale sans toutes les tester, vol.65

, Deux approches sont mises en oeuvre pour réduire la complexité du problème et accélérer sa résolution. La première approche consisteà décomposer le problème, en divisant l'ensemble des serveurs en clusters, qui peuventêtre formés de manière statique

, La seconde approche consisteà résoudre le problème de manière itérative

, Les métaheuristiques partent d'un ensemble de solutions aléatoires et explorent l'entourage des meilleures avec une probabilité plus forte que les moins bonnes

, Utilisation de l'apprentissage automatique pour le placement des VMs Avec l'évolution de l'état de la plateforme suite au démarrage/arrêt de VMs ouà un changement d'utilisation, le fournisseur doit ré-optimiser l'allocation des ressources. Pour compenser le temps nécessaire pour rechercher et implémenter une configuration optimale, il est nécessaire d'anticiper les changements. Plusieurs travaux utilisent des techniques d'apprentissage automatique pour prédire l'état du cloud. L'apprentissage automatique chercheà résoudre des problèmes en trouvant des règles dans un ensemble de données

É. Le-a-sensibilité-de-l'algorithme-rtabf-fit and . Best-fit, Nous recensons

, Présentation des algorithmes de placement des VMs en ligne Les algorithmes de placement en ligne sont utilisés pour le placement initial des VMs, pas pour la consolidation des VMs existantes. Les deux algorithmes les plus connus sont Any-Fit (AF), qui choisit un serveur au hasard parmi ceux disposant d'assez de ressources; et Best-Fit, qui choisit le serveur sur lequel le placement de la VM laissera le moins de ressources libres

, Tandis que AF et BF se basent uniquement sur la quantité de ressources demandée

, RTABF combine l'objectif de BF avec l'objectif de minimiser le temps d'utilisation des serveurs, et donc l'énergie consommée. Pour cela, RTABF co-localise les VMs qui s'éteindront en même temps. La figure 7.5a compare l'exécution des algorithmes BF et RTABF, et montre comment la prise en compte de la durée de vie peut servir aéconomiser l'énergie

, Pour la trace d'exécution, nous avons sélectionné dixéchantillons aléatoires de la trace de Google [96] afin d'obtenir des résultats statistiquement fiablesà partir de simulations rapides, et aussi afin de comparer nos résultats avec l'évaluation originale de RTABF. Nous avons simulé la présence d'erreur de prédiction en rajoutantà la durée de vie des VMs un bruit Gaussien

, Résultats La figure 7.6 présente la moyenne et la déviation standard deséconomies d'énergie réalisées par

R. Rtabf, . Af, and . Bf, pour différents niveaux d'erreur de prédiction. Indépendamment du niveau d'erreur de prédiction, RTABFéconomise 25% d'énergie par rapportà AF. Par contre, RTABF n'économise pas d'énergie par rapportà BF

, CONCLUSION GÉNÉRALE Ce résultat négatif pourraitêtre expliqué par la relation entre la durée de vie des VMs et la fraction de ressources consommée sur l'infrastructure: "La plupart des VMs ont une durée de vie courte, mais contribuent peuà l'utilisation. Ainsi, les 98% de VMs qui durent plus d'une journée consomment moins de 20% des ressources, vol.135

, Le poids important des longues VMs pourrait empêcher l'algorithme

, Nous avons simulé le placement d'un flux de VMs d'après la trace de Google pour différents niveaux d'erreurs de prédiction, et rapporté la consommation d'énergie sous RTABF avec celle sous Any-Fit et Best-Fit. Nos résultats montrent que RTABF n'est pas meilleur que Best-Fit, possiblement parce que les VMs de longue durée de vie consomment la plupart des ressources et empêchent donc l'algorithme d'éteindre les serveurs pouréconomiser de l'énergie. Nous concluons donc que le placement d'une VM avec RTABF n'est pas un cas d'usage viable pour les prédictions de durée de vie

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