Performance modeling of cloud computing centers
Notice bibliographique
Résumé
Cloud computing is a general term for system architectures that involves delivering hosted services over the Internet, made possible by significant innovations in virtualization and distributed computing, as well as improved access to high-speed Internet. A cloud service differs from traditional hosting in three principal aspects. First, it is provided on demand, typically by the minute or the hour; second, it is elastic since the user can have as much or as little of a service as they want at any given time; and third, the service is fully managed by the provider—user needs little more than computer and Internet access. Typically a contract is negotiated and agreed between a customer and a service provider; the service provider is required to execute service requests from a customer within negotiated quality of service (QoS) requirements for a given price. Due to dynamic nature of cloud environments, diversity of user's requests, resource virtualization, and time dependency of load, provides expected quality of service while avoiding over-provisioning is not a simple task. To this end, cloud provider must have efficient and accurate techniques for performance evaluation of cloud computing centers. The development of such techniques is the focus of this thesis. This thesis has two parts. In first part, Chapters 2, 3 and 4, monolithic performance models are developed for cloud computing performance analysis. We begin with Poisson task arrivals, generally distributed service times, and a large number of physical servers. Later on, we extend our model to include finite buffer capacity, batch task arrivals, and virtualized servers with a large number of virtual machines in each physical machine. However, a monolithic model may suffer from intractability and poor scalability due to large number of parameters. Therefore, in the second part of the thesis (Chapters 5 and 6) we develop and evaluate tractable functional performance sub-models for different servicing steps in a complex cloud center and the overall solution obtains by iteration over individual sub-model solutions. We also extend the proposed interacting analytical sub-models to capture other important aspects including pool management, power consumption, resource assigning process and virtual machine deployment of nowadays cloud centers. Finally, a performance model suitable for cloud computing centers with heterogeneous requests and resources using interacting stochastic models is proposed and evaluated.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».