pr\u00e9sentent<\/p>\n\n\n\n
Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en :<\/p>\n\n\n\n
le JEUDI 30 NOVEMBRE 2023 \u00e0 14h00<\/p>\n\n\n\n
\u00e0 Amphi 2, 19 place Marguerite Perey 91120 Palaiseau<\/p>\n\n\n\n
Membres du jury :<\/strong><\/p>\n\n\n\n M. Tijani CHAHED<\/strong>, Full professor, Telecom SudParis, FRANCE – Directeur de these R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n Dans cette th\u00e8se, nous consid\u00e9rons le Edge Computing (EC) comme un environnement multi-tenant o\u00f9 les Op\u00e9rateurs R\u00e9seau (NOs) poss\u00e8dent des ressources en p\u00e9riph\u00e9rie d\u00e9ploy\u00e9es dans les stations de base, les bureaux centraux et\/ou les bo\u00eetiers intelligents, les virtualisent, et permettent aux Fournisseurs de Services tiers (SPs) – ou tenants – de distribuer une partie de leurs applications en p\u00e9riph\u00e9rie afin de r\u00e9pondre aux demandes des utilisateurs. Les SPs aux besoins h\u00e9t\u00e9rog\u00e8nes coexistent en p\u00e9riph\u00e9rie, allant des Communications Ultra-Fiables \u00e0 Latence Ultra-Basse (URLLC) pour le contr\u00f4le des v\u00e9hicules ou des robots, \u00e0 la Communication de Type Machine Massive (mMTC) pour l’Internet des Objets (IoT) n\u00e9cessitant un grand nombre de dispositifs connect\u00e9s, en passant par les services multim\u00e9dias tels que la diffusion vid\u00e9o et la R\u00e9alit\u00e9 Augment\u00e9e\/Virtuelle (AR\/VR), dont la qualit\u00e9 d’exp\u00e9rience d\u00e9pend fortement des ressources disponibles. Les SPs orchestrent ind\u00e9pendamment leur ensemble de microservices, ex\u00e9cut\u00e9s dans des conteneurs, qui peuvent \u00eatre facilement r\u00e9pliqu\u00e9s, migr\u00e9s ou arr\u00eat\u00e9s. Chaque SP peut s’adapter aux ressources allou\u00e9es par le NO, en d\u00e9cidant s’il doit ex\u00e9cuter des microservices sur les appareils, les n\u0153uds en p\u00e9riph\u00e9rie ou dans le cloud. L’objectif de cette th\u00e8se est de promouvoir l’\u00e9mergence de d\u00e9ploiements r\u00e9els du \u00ab\u00a0v\u00e9ritable\u00a0\u00bb EC dans de vrais r\u00e9seaux, en montrant l’utilit\u00e9 que les NOs peuvent tirer de l’EC. Nous croyons que cela peut contribuer \u00e0 encourager l’engagement concret et les investissements des NOs dans l’EC. \u00c0 cette fin, nous proposons de concevoir de nouvelles strat\u00e9gies bas\u00e9es sur les donn\u00e9es qui allouent efficacement les ressources entre les SPs h\u00e9t\u00e9rog\u00e8nes, en p\u00e9riph\u00e9rie, appartenant au NO, afin d’optimiser ses objectifs pertinents, tels que la r\u00e9duction des co\u00fbts, la maximisation des revenus et l’am\u00e9lioration de la Qualit\u00e9 de Service (QoS) per\u00e7ue par les utilisateurs finaux, en termes de latence, de fiabilit\u00e9 et de d\u00e9bit, tout en r\u00e9pondant aux exigences des SPs. Cette th\u00e8se pr\u00e9sente une perspective sur la mani\u00e8re dont les NOs, les seuls propri\u00e9taires de ressources en p\u00e9riph\u00e9rie, peuvent extraire de la valeur gr\u00e2ce \u00e0 la mise en \u0153uvre de l’EC dans un environnement multi-tenant. En promouvant cette vision de l’EC et en la soutenant par des r\u00e9sultats quantitatifs et une analyse approfondie, cette th\u00e8se fournit principalement aux NOs des conclusions susceptibles d’influencer les strat\u00e9gies de d\u00e9cision concernant le d\u00e9ploiement futur de l’EC. Cela pourrait favoriser l’\u00e9mergence de nouvelles applications \u00e0 faible latence et \u00e0 forte intensit\u00e9 de donn\u00e9es, telles que la r\u00e9alit\u00e9 augment\u00e9e haute r\u00e9solution, qui ne sont pas envisageables dans le cadre actuel du Cloud Computing (CC). Une autre contribution de la th\u00e8se est qu’elle propose des solutions bas\u00e9es sur des m\u00e9thodes novatrices exploitant la puissance de l’optimisation bas\u00e9e sur les donn\u00e9es. En effet, nous adaptons des techniques de pointe issues de l’Apprentissage par Renforcement (RL) et de la prise de d\u00e9cision s\u00e9quentielle au probl\u00e8me pratique de l’allocation des ressources en EC. Ce faisant, nous parvenons \u00e0 r\u00e9duire le temps d’apprentissage des strat\u00e9gies adopt\u00e9es \u00e0 des \u00e9chelles compatibles avec la dynamique de l’EC, gr\u00e2ce \u00e0 la conception soign\u00e9e de mod\u00e8les d’estimation int\u00e9gr\u00e9s au processus d’apprentissage. Nos strat\u00e9gies sont con\u00e7ues de mani\u00e8re \u00e0 ne pas violer les garanties de confidentialit\u00e9 essentielles pour que les SPs acceptent d’ex\u00e9cuter leurs calculs en p\u00e9riph\u00e9rie, gr\u00e2ce \u00e0 l’environnement multi-tenant.<\/p>\n\n\n\n We consider in this thesis Edge Computing (EC) as a multi-tenant environment where Network Operators (NOs) own edge resources deployed in base stations, central offices and\/or smart boxes, virtualize them and let third party Service Providers (SPs) – or tenants – distribute part of their applications in the edge in order to serve the requests sent by the users. SPs with heterogeneous requirements coexist in the edge, ranging from Ultra-Reliable Low Latency Communications (URLLC) for controlling cars or robots, to massive Machine Type Communication (mMTC) for Internet of Things (IoT) requiring a massive number of connected devices, to media services, such as video streaming and Augmented\/Virtual Reality (AR\/VR), whose quality of experience is strongly dependant on the available resources. SPs independently orchestrate their set of microservices, running on containers, which can be easily replicated, migrated or stopped. Each SP can adapt to the resources allocated by the NO, deciding whether to run microservices in the devices, in the edge nodes or in the cloud. We aim in this thesis to advance the emergence of real deployments of the \u201ctrue\u201d EC in real networks, by showing the utility that NOs can collect thanks to EC. We believe that this can contribute to encourage concrete engagement and investments engagement of NOs in EC. For this, we point to design novel data-driven strategies that efficiently allocate resources between heterogeneous SPs, at the edge owned by the NO, in order to optimize its relevant objectives, e.g., cost reduction, revenue maximization and better Quality of Service (QoS) perceived by end users, in terms of latency, reliability and throughput, while satisfying the SPs requirements. This thesis presents a perspective on how NOs, the sole owners of resources at the far edge (e.g., at base stations), can extract value through the implementation of EC within a multi-tenant environment. By promoting this vision of EC and by supporting it via quantitative results and analysis, this thesis provides, mainly to NOs, findings that can influence decision strategies about the future deployment of EC. This might foster the emergence of novel low-latency and data-intensive applications, such as high resolution augmented reality, which are not feasible in the current Cloud Computing (CC) setting. Another contribution of the thesis it that it provides solutions based on novel methods that harness the power of data-driven optimization.We indeed adapt cutting-edge techniques from Reinforcement Learning (RL) and sequential decision making to the practical problem of resource allocation in EC. In doing so, we succeed in reducing the learning time of the adopted strategies up to scales that are compatible with the EC dynamics, via careful design of estimation models embedded in the learning process. Our strategies are conceived in order not to violate the confidentiality guarantees that are essential for SPs to accept running their computation at the EC, thanks to the multi-tenant setting.<\/p>\n","protected":false},"excerpt":{"rendered":" L’Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Paris et le Laboratoire de recherche SAMOVAR – Services r\u00e9partis, Architectures, Mod\u00e9lisation, Validation, Administration des R\u00e9seaux pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Monsieur Ayoub BEN AMEUR Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en 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M. Anastasios GIOVANIDIS<\/strong>, Charg\u00e9 de recherche, Ericsson, FRANCE – Rapporteur
M. Antonio CAPONE<\/strong>, Full professor, Politecnico di Milano, ITALIE – Rapporteur
Mme Ana BUSIC<\/strong>, Research Scientist, Ecole Normale Sup\u00e9rieur – PSL, FRANCE – Examinateur
M. Daniel KOFMAN<\/strong>, Professor, Telecom Paris, FRANCE – Examinateur
M. Andrea ARALDO<\/strong>, Assistant professor, Telecom SudParis , FRANCE – CoDirecteur de these<\/p>\n\n\n\n\u00ab L’Intelligence artificielle pour l’allocation des ressources dans le Multi-tenant Edge Computing \u00bb<\/h2>\n\n\n\n
pr\u00e9sent\u00e9 par Monsieur Ayoub BEN AMEUR<\/h2>\n\n\n\n
Abstract :<\/strong><\/p>\n\n\n\n