End-to-End latency reduction techniques for delay-sensitive applications in satellite-based edge computing networks / Fatima Zahra Zaoui

Public ISBD Titre : End-to-End latency reduction techniques for delay-sensitive applications in satellite-based edge computing networks Type de document : document multimédia Auteurs : Fatima Zahra Zaoui, Auteur ; Messaoud Babaghayou, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2025 Importance : 67 p. Accompagnement : CD ROM Note générale : Option : Networks,systems and distributed applications Langues : Anglais Mots-clés : Satellite  Edge Computing  Task Offloading  End-to-End Latency  Mist Layer  Intelligent orchestration  Delay-sensitive applications Résumé : With the advent of satellite-edge convergence, which is transforming distributed computing to make services real-time and intelligent, the management of task offloading more efficiently is more important than before.This work addresses the issue of minimizing the end-to-end delay for delay-critical applications in satellite-fueled mist computing systems. This work formulates a new task orchestration algorithm named IsoLink, which was specifically developed and tuned for mist-layer offloading and optimized to classify tasks based on context and assign them wisely to the appropriate virtual machines.IsoLink functions by normalizing the delay and execution parameters, taking into account the type and resource proximity to the task, to enable optimal deployment. By simulating comprehensively using the SatEdgeSim framework, the algorithm performed well in terms of reduced latency, energy savings, and enhanced task success ratio. The evaluation was conducted across various performance metrics and also compared with algorithms such as the Weighte_Greedy, Round_Robin, and Random_VM, and proved the efficacy of IsoLink. Although the method is well-suited for services requiring low latency, future improvements make satellite mist computing environments more adaptive and efficient in operation. note de thèses : Mémoire de master en informatique End-to-End latency reduction techniques for delay-sensitive applications in satellite-based edge computing networks [document multimédia] / Fatima Zahra Zaoui, Auteur ; Messaoud Babaghayou, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 67 p. + CD ROM. Option : Networks,systems and distributed applications Langues : Anglais Mots-clés : Satellite  Edge Computing  Task Offloading  End-to-End Latency  Mist Layer  Intelligent orchestration  Delay-sensitive applications Résumé : With the advent of satellite-edge convergence, which is transforming distributed computing to make services real-time and intelligent, the management of task offloading more efficiently is more important than before.This work addresses the issue of minimizing the end-to-end delay for delay-critical applications in satellite-fueled mist computing systems. This work formulates a new task orchestration algorithm named IsoLink, which was specifically developed and tuned for mist-layer offloading and optimized to classify tasks based on context and assign them wisely to the appropriate virtual machines.IsoLink functions by normalizing the delay and execution parameters, taking into account the type and resource proximity to the task, to enable optimal deployment. By simulating comprehensively using the SatEdgeSim framework, the algorithm performed well in terms of reduced latency, energy savings, and enhanced task success ratio. The evaluation was conducted across various performance metrics and also compared with algorithms such as the Weighte_Greedy, Round_Robin, and Random_VM, and proved the efficacy of IsoLink. Although the method is well-suited for services requiring low latency, future improvements make satellite mist computing environments more adaptive and efficient in operation. note de thèses : Mémoire de master en informatique

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Machine learning for link prediction in complex networks / Messaoud Babaghayou

Public ISBD Titre : Machine learning for link prediction in complex networks Type de document : texte manuscrit Auteurs : Messaoud Babaghayou, Auteur ; Abdallah Lakhdari, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2016 Importance : 79 p. Format : 30 cm. Accompagnement : 1 disque optique numérique Note générale : Option : Networks, systems and distributed applications ( Réseaux,systèmes et applications réparties) Langues : Anglais Mots-clés : Machine Learning  Link Prediction  Complex Networks  Supervised Leaning  Unsupervised Learning  Classification  Node-based Metrics Résumé : Nowdays, networks are omnipresent. The study and understanding of these networks become a greater need. The purpose of this work, is to investigate link prediction task in complex networks using Machine learning techniques. In fact, we propose two approaches to perform link prediction: supervised and unsupervised one. In both techniques a link or a pair of nodes is characterized by several features based on network topology-based metrics. In addition, we investigate many combined features. Concerning the supervised approach, we investigate the KNN and decision tree methods to build the link prediction models. While in the unsupervised approach, we rely on ranking strategy. An experimental study is performed on real networks. The results show that the supervised approach using gathered features reaches good performances with 84% f-measure. note de thèses : Mémoire de master en informatique Machine learning for link prediction in complex networks [texte manuscrit] / Messaoud Babaghayou, Auteur ; Abdallah Lakhdari, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2016 . - 79 p. ; 30 cm. + 1 disque optique numérique. Option : Networks, systems and distributed applications ( Réseaux,systèmes et applications réparties) Langues : Anglais Mots-clés : Machine Learning  Link Prediction  Complex Networks  Supervised Leaning  Unsupervised Learning  Classification  Node-based Metrics Résumé : Nowdays, networks are omnipresent. The study and understanding of these networks become a greater need. The purpose of this work, is to investigate link prediction task in complex networks using Machine learning techniques. In fact, we propose two approaches to perform link prediction: supervised and unsupervised one. In both techniques a link or a pair of nodes is characterized by several features based on network topology-based metrics. In addition, we investigate many combined features. Concerning the supervised approach, we investigate the KNN and decision tree methods to build the link prediction models. While in the unsupervised approach, we rely on ranking strategy. An experimental study is performed on real networks. The results show that the supervised approach using gathered features reaches good performances with 84% f-measure. note de thèses : Mémoire de master en informatique

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