Notice bibliographique
Résumé
Fog computing is a paradigm in which resources are close to the end-users, complementing cloud computing and allowing the execution of workloads with reduced latency.Fog computing enables the deployment of new applications with low-latency requirements and can improve the execution of typical cloud applications.Fog computing relies on fog nodes, facilities with processing, networking, and storage resources placed in the continuum between end-users and the cloud.An early step in the design of a fog computing infrastructure is the location of fog nodes.This decision is crucial because end-users are mobile and, consequently, fog nodes must be deployed in different geographical regions to meet the latency requirements of applications.Moreover, users' demands are variable in time.Therefore, the location of fog nodes as well as their hardware configuration must take into account the variable demands of end-users in time and space.This thesis proposes solutions to the location of fog nodes considering different aspects of a fog computing infrastructure.First, a solution to reduce the capital expenditure of the infrastructure is proposed.Second, the location of fog nodes is decided so that the end-user devices can reduce their energy consumption.Third, solutions with mobile fog nodes mounted on unmanned aerial vehicles (UAVs) are investigated.Finally, a resource allocation mechanism for fog-cloud infrastructures is proposed.All solutions aim at providing the best infrastructure for end-users running workloads with low-latency requirements.Different solutions can be individually applied or combined.In this thesis, the fog node location problem is formulated as linear programming models, and different heuristic algorithms are proposed to deal with scenarios representing metropolitan areas.All evaluations were made using simulations.The evaluation of solutions in this thesis was made using simulations of metropolitan areas inhabited by millions of people.Fog nodes are characterized by their location and processing capacity.UAVs with operations limited by batteries are also simulated as fog nodes.Results show that, although dealing with variable demands is challenging, different solutions are possible to reduce underutilization of resources, such as slightly reducing the acceptance of requests to obtain large savings with the deployment costs, or employing UAVs to process peaks of demands.The proposed algorithms were shown to be scalable.The work in this thesis pushes the boundaries of the knowledge of the fog node location problem and can be adapted for future work.
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 | 0,000 | 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 ».