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Σχολή Επιστημών και Τεχνολογίας της Πληροφορίας

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Πλοήγηση Σχολή Επιστημών και Τεχνολογίας της Πληροφορίας ανά Θέμα "5G/B5G/6G communication networks"

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  • Μικρογραφία εικόνας
    Τεκμήριο
    Resource allocation, content recommendations and online learning mechanisms for mobile edge computing
    (05/23/2022) Χατζηελευθερίου, Λίβια-Έλενα; Athens University of Economics and Business, Department of Informatics; Polyzos, George; Toumpis, Stavros; Iosifidis, George; Dimakis, Antonios; Siris, Vasileios; Stamoulis, Georgios; Koutsopoulos, Iordanis
    The Mobile Edge Computing (MEC) paradigm brings computing and cache capacity resources in the proximity of users. It gives rise to a new ecosystem of services, such as Augmented Reality (AR) ones, while reducing the latency that is experienced by users and lowering network service costs. The main challenges that MECfaces are related to the scarcity of resources at the network edge, the unpredictability of important system parameters, such as traffic, content and computation demand, and the ultra-low latency requirements that must be satisfied.In this thesis we deal with the challenges above, towards the optimization of two MEC goals: content delivery and real-time analytics at the edge of the network. We present resource allocation mechanisms and methods that automate the resource allocation, for fifth-generation (5G), Beyond-5G (B5G) and sixth-generation (6G) communication systems, accounting for edge resources such as caches, computational resources of mobiledevices and edge servers, bandwidth and energy. We tackle both offline and Online Learning (OL) instances of optimization problems that span content recommendationsand caching, user association and allocation of computing resources. We use a variety of mathematical tools to solve these problems, such as combinatorial optimization, convex optimization and Online Convex Optimization (OCO), a special case of OL. We analyse and we exploit the structural properties of the formulated optimization problems, either by designing algorithms ex novo, or by adapting existing techniques to our settings. We provide cost-efficient, fast and elegant solutions with provable performance guarantees, for a variety of important problems that arise within the MEC context. Overall, this Ph.D. thesis tackles a set of important optimization problems that arise in the context of edge computing and networking. We present novel problem formulations and algorithms that lead to solutions with provable performance guarantees, bringing the Mobile Edge Computing (MEC) paradigm a step closer to its practical realization.