Optimized content caching strategies for multi-access edge computing (MEC)-assisted future cellular networks
(English) Handling the tsunami of multimedia content is a big challenge for heterogeneous cellular networks. Serving large volumes of content from the central system to end-users, through a bandwidth-limited network, at peak time leads to network congestion. Typically, popular contents impede networ...
| Autor: | |
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/690294 |
| Acceso en línea: | http://hdl.handle.net/10803/690294 https://dx.doi.org/10.5821/dissertation-2117-404647 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 |
| Sumario: | (English) Handling the tsunami of multimedia content is a big challenge for heterogeneous cellular networks. Serving large volumes of content from the central system to end-users, through a bandwidth-limited network, at peak time leads to network congestion. Typically, popular contents impede network performance and incur a high cost because they are redundantly transmitted to users. Also, some contents are too far from users, causing service delays. Owing to the above reasons, the network performance deteriorates, and meeting the required quality of experience becomes difficult. One way to improve HCN performance is through the use of caching of popular contents. This research proposes and analyzes content caching optimization strategies for several scenarios of cellular networks enhanced with multi-access edge computing. Several studies have shown that backhaul congestion and service latency can be reduced by applying efficient content caching policies. However, a challenging issue is that the network edges have limited resources, such as cache size and computation. Due to such limitations, how to effectively exploit the available network resource and cache contents optimally remains to be an open-ended question. Content caching strategies have been the subject of several research efforts; however, in most works, the accurate modeling of the caching problem is a challenging open problem. In addition, most of them employ intractable optimization tools, which depend on greedy algorithms and heuristics. Most strategies must also be scaled into large networks and have computationally feasible solutions. More specifically, the complex joint-caching problem remains open. This dissertation aims to fill these gaps and contribute to modeling real-network characteristics using advanced combinatorial optimization tools. We propose different content caching formulations and design novel optimization strategies. The general approach is to effectively model caching schemes using popularity-based selection problems and solve the models using efficient algorithms. We devise an optimal caching scheme using dynamic programming. A more complex but exact caching strategy is proposed, where heterogeneous caching edges are clustered to offload the central system. Furthermore, we propose three demand-aware caching strategies, where a source caches contents to multiple caching edges, and the content popularity differs for each edge. Finally, we propose a demand-aware joint caching problem modeling where the content placement and delivery are mutually optimized. The performances of the proposed strategies are thoroughly evaluated using extensive system-level simulations, comparing them with existing content caching strategies. The numerical results show that the proposed strategies outperform baseline strategies in terms of relevant key performance indicators. Also, the associated Multi-access Edge Computing (MEC) functionalities are omputationally feasible. Finally, useful cache design guidelines are set for various wireless network scenarios, contributing to the future design of emerging technologies and practical business models. |
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