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With the development of Internet of Things (IoT) and communicationtechnology, the number of next-generation IoT devices has increasedexplosively, and the delay requirement for content requests is becomingprogressively higher. Fortunately, the edge-caching scheme can satisfy users'demands for low latency of content. However, the existing caching schemes arenot smart enough. To address these challenges, we propose a human-like hybridcaching architecture based on the software defined edge cloud, whichsimultaneously considers the content popularity and the fine-grained usercharacteristics. Then, an optimization problem with a caching hit ratio as anoptimization objective is formulated. To solve this problem, usingreinforcement learning, we design a human-like hybrid caching algorithm.Extensive experiments show that compared with popular caching schemes,human-like hybrid caching schemes can improve the cache hit ratio by 20%.

Human-Like Hybrid Caching in Software-defined Edge Cloud