University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Titre : Task offloading, Scheduling and Resource Allocation Type de document : texte imprimé Auteurs : Maroua Rehahla ; Fatima Zohra Saadoune ; Djamila Mechta, Directeur de thèse Editeur : Setif:UFA Année de publication : 2023 Importance : 1 vol. (66 f.) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Internet of Things Edge Computing Task Scheduling Resource Allocation Optimization Index. décimale : 004 Informatique Résumé : Task offloading, scheduling and resource allocation are the main elements of edge computing.
This Master thesis aims to find a new technique to do the offloading, the task scheduling, and the resource
allocation, based on artificial intelligence (AI). we will talk about the main definitions, concepts, analyzes
and discusses some surveys and related works to offloading, task scheduling and resource allocation. Our
contribution is proposed a new hybrid method for task offloading and resource allocation based on artificial
intelligence (AI) especially the K-means algorithm (KMA) for the offloading decision and the genetic algorithm
(GA) for the scheduling and resources allocation. Finally, this method takes advantages of both the artificial
intelligence (k-means clustering) and genetic algorithm (GA) to optimize the makespan, the bandwidth and
the energy consumption. After comparing our work with authors works, we have found that our proposed is
we notice that the proposed algorithm is efficient in terms of makespan, bandwidth and energy consumptionCôte titre : MAI/0749 En ligne : https://drive.google.com/file/d/1RvsRQn9ecY65LzPjLco6gwyt0IDoU3uL/view?usp=drive [...] Format de la ressource électronique : Task offloading, Scheduling and Resource Allocation [texte imprimé] / Maroua Rehahla ; Fatima Zohra Saadoune ; Djamila Mechta, Directeur de thèse . - [S.l.] : Setif:UFA, 2023 . - 1 vol. (66 f.) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Internet of Things Edge Computing Task Scheduling Resource Allocation Optimization Index. décimale : 004 Informatique Résumé : Task offloading, scheduling and resource allocation are the main elements of edge computing.
This Master thesis aims to find a new technique to do the offloading, the task scheduling, and the resource
allocation, based on artificial intelligence (AI). we will talk about the main definitions, concepts, analyzes
and discusses some surveys and related works to offloading, task scheduling and resource allocation. Our
contribution is proposed a new hybrid method for task offloading and resource allocation based on artificial
intelligence (AI) especially the K-means algorithm (KMA) for the offloading decision and the genetic algorithm
(GA) for the scheduling and resources allocation. Finally, this method takes advantages of both the artificial
intelligence (k-means clustering) and genetic algorithm (GA) to optimize the makespan, the bandwidth and
the energy consumption. After comparing our work with authors works, we have found that our proposed is
we notice that the proposed algorithm is efficient in terms of makespan, bandwidth and energy consumptionCôte titre : MAI/0749 En ligne : https://drive.google.com/file/d/1RvsRQn9ecY65LzPjLco6gwyt0IDoU3uL/view?usp=drive [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0749 MAI/0749 Mémoire Bibliothéque des sciences Anglais Disponible
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