In recent years, information-centric networks (ICNs) have gained attention from the research and industry communities as an efficient and reliable content distribution network paradigm, especially to address content-centric and bandwidth-needed applications together with the heterogeneous requirements of emergent networks, such as the Internet of Things (IoT), Vehicular Ad-hoc NETwork (VANET) and Mobile Edge Computing (MEC). In-network caching is an essential part of ICN architecture design, and the performance of the overall network relies on caching policy efficiency. Therefore, a large number of cache replacement strategies have been proposed to suit the needs of different networks. The literature extensively presents studies on the performance of the replacement schemes in different contexts. The evaluations may present different variations of context characteristics leading to different impacts on the performance of the policies or different results of most suitable policies. Conversely, there is a lack of research efforts to understand how the context characteristics influence policy performance. In this direction, we conducted an extensive study of the ICN literature through a Systematic Literature Review (SLR) process to map reported evidence of different aspects of context regarding the cache replacement schemes. Our main findings contribute to the understanding of what is a context from the perspective of cache replacement policies and the context characteristics that influence cache behavior. We also provide a helpful classification of policies based on context dimensions used to determine the relevance of contents. Further, we contribute with a set of cache-enabled networks and their respective context characteristics that enhance the cache eviction process.
A exploração de padrões do comportamento humano é tema central e norteador no desenvolvimento de novas aplicações e soluções tecnológicas. No entanto, poucos trabalhos investigam como hábitos de usuários podem melhorar o desempenho de arquiteturas de Redes Centradas na Informação. Este trabalho apresenta uma análise de perfis comportamentais de usuários de música e como diferentes perfis influenciam o desempenho de políticas de substituição de cache. Os resultados de um estudo experimental utilizando o ndnSIM com traces reais de diversos usuários, mostram que os hábitos do usuário são fatores determinantes na escolha de uma política de substituição de cache otimizada. As investigações também revelam que a distribuição de popularidade das músicas segue uma aproximação da Lei de Benford, e é possível diferenciar o perfil dos usuários de acordo com o comportamento da curva de Benford das músicas acessadas.
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