With the development of 5G mobile networks, people’s demand for network response speed and services has increased to meet the needs of a large amount of data traffic, reduce the backhaul load caused by frequently requesting the same data (or content). The file is pre-stored in the base station by the edge device, and the user can directly obtain the requested data in the local cache without remotely. However, changes in popularity are difficult to capture, and data is updated more frequently through the backhaul. In order to reduce the number of backhauls and provide caching services for users with specific needs, we can provide proactive caching with users without affecting user activity. We propose a content caching strategy based on mobility prediction and joint user prefetching (MPJUP). The policy predicts the prefetching device data by predicting the user’s movement position with respect to time by the mobility of the user and then splits the partial cache space for prefetching data based on the user experience gain. Besides, we propose to reduce the backhaul load by reducing the number of content backhauls by cooperating prefetch data between the user and the edge cache device. Experimental analysis shows that our method further reduces the average delay and backhaul load, and the prefetch method is also suitable for more extensive networks.

中文翻译：

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移动边缘网络中基于移动性预测和联合用户预取的内容缓存

随着5G移动网络的发展，人们对网络响应速度和服务的需求不断增长，以满足大量数据流量的需求，减轻了因频繁请求相同数据（或内容）而引起的回程负载。该文件由边缘设备预先存储在基站中，并且用户可以直接在本地缓存中获取请求的数据，而无需远程获取。但是，难以捕捉到流行的变化，并且通过回程更频繁地更新数据。为了减少回传的次数并为有特定需求的用户提供缓存服务，我们可以在不影响用户活动的情况下为用户提供主动缓存。我们提出了一种基于移动性预测和联合用户预取（MPJUP）的内容缓存策略。该策略通过根据用户的移动性来预测用户相对于时间的移动位置来预测预取设备数据，然后基于用户体验增益来分割用于预取数据的部分缓存空间。此外，我们建议通过在用户和边缘缓存设备之间协作预取数据来减少内容回程的数量，从而减少回程负载。实验分析表明，我们的方法进一步降低了平均延迟和回程负载，预取方法也适用于更广泛的网络。我们建议通过在用户和边缘缓存设备之间协作预取数据来减少内容回程的数量，从而减少回程负载。实验分析表明，我们的方法进一步降低了平均延迟和回程负载，预取方法也适用于更广泛的网络。我们建议通过在用户和边缘缓存设备之间协作预取数据来减少内容回程的数量，从而减少回程负载。实验分析表明，我们的方法进一步降低了平均延迟和回程负载，预取方法也适用于更广泛的网络。