Recomendação de conteúdo ciente de recursos como estratégia para cache na borda da rede em sistemas 5G

• Ano de defesa: 2023
• Autor(a) principal: Monção, Ana Claudia Bastos Loureiro
• Orientador(a): Cardoso, Kleber Vieira
• Banca de defesa: Cardoso, Kleber Vieira, Corrêa, Sand Luz, Soares, Telma Woerle de Lima, Rosa, Thierson Couto, Fonseca, Anelise Munaretto
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RMG)
• País: Brasil
• Palavras-chave em Português: MEC; 5G; Cache na borda da rede; Sistemas de recomendação; Streaming de vídeo; QoE; Predição de CQI
• Palavras-chave em Inglês: Caching at the edge of the network; Recommender systems; CQI prediction
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/13082

Recently, the coupling between content caching at the wireless network edge and video recommendation systems has shown promising results to optimize the cache hit and improve the user experience. However, the quality of the UE wireless link and the resource capabilities of the UE are aspects that impact the user experience and that have been neglected in the literature. In this work, we present a resource-aware optimization model for the joint task of caching and recommending videos to mobile users. We also present a heuristic created to solve the problem more quickly. The goal is to maximize the cache hit ratio and the user QoE (concerning content preferences and video representations) under the constraints of UE capabilities and the availability of network resources by the time of the recommendation. We evaluate our proposed model using a video catalog derived from a real-world video content dataset (from the MovieLens project), real- world video representations and actual historical records of Channel Quality Indicators (CQI) representing user mobility. We compare the performance of our proposal with a state-of-the-art caching and recommendation method unaware of computing and network resources. Results show that our approach significantly increases the user’s QoE and still promotes a gain in effective cache hit rate.