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Nouvelles stratégies de mise en cache et de mobilité intelligentes pour MEC / architectures basées ICN

Abstract : Mobile edge computing (MEC) concept proposes to bring the computing and storage resources in close proximity to the end user by placing these resources at the network edge. The motivation is to alleviate the mobile core and to reduce latency for mobile users due to their close proximity to the edge. MEC servers are candidates to host mobile applications and serve web contents. Edge caching is one of the most emerging technologies recognized as a content retrieval solution in the edge of the network. It has been also considered as enabling technology of mobile edge computing that presents an interesting opportunity to perform caching services. Particularly, the MEC servers are implemented directly at the base stations which enable edge caching and ensure deployment in close-proximity to the mobile users. However, the integration of servers in mobile edge computing environment (base stations) complicates the energy saving issue because the power consumed by mobile edge computing servers is costly especially when the load changes dynamically over time. Furthermore, users with mobile devices arise their demands, introducing the challenge of handling such mobile content requests beside the limited caching size. Thus, it is necessary and crucial for caching mechanisms to consider context-aware factors, meanwhile most existing studies focus on cache allocation, content popularity and cache design. In this thesis, we present a novel energy-efficient fuzzy caching strategy for edge devices that takes into consideration four influencing features of mobile environment, while introducing a hardware implementation using Field-Programmable Gate Array (FPGA) to cut the overall energy requirements. Performing an adequate caching strategy on MEC servers opens the possibility of employing artificial intelligence (AI) techniques and machine learning at mobile network edges. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. Context awareness enables the cache to be aware of its environment, while intelligence enables each cache to make the right decisions of selecting appropriate contents to be cached so that to maximize the caching performance. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we extended our fuzzy-caching system into a modified reinforcement learning model. The proposed framework aims to maximize the cache hit rate and requires a multi awareness. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.
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Submitted on : Friday, February 26, 2021 - 1:06:08 PM
Last modification on : Monday, February 21, 2022 - 3:38:11 PM
Long-term archiving on: : Thursday, May 27, 2021 - 6:37:48 PM


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  • HAL Id : tel-03153449, version 1



Sarra Mehamel. Nouvelles stratégies de mise en cache et de mobilité intelligentes pour MEC / architectures basées ICN. Informatique mobile. Conservatoire national des arts et metiers - CNAM; Université Mouloud Mammeri (Tizi-Ouzou, Algérie), 2020. Français. ⟨NNT : 2020CNAM1284⟩. ⟨tel-03153449⟩



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