The 5G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5G UDNs. In this paper, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76m, with up to 80% of the unknown areas.

翻译：5G网络极大地推动了移动用户和新型应用的增长，随着用户对大量热门内容的请求激增，由此产生的内容交付服务（CDS）给移动服务提供商带来了沉重负担。作为智能网络管理的关键任务，理解和预测CDS的分布有助于现代网络服务的多项任务，例如资源供应和内容交付网络的主动内容缓存。然而，以超密集网络（UDN）为代表的新型泛在网络架构的革新使该任务极具挑战性。具体而言，传统方法面临空间精度不足、缺乏泛化能力以及用户请求的多特征复杂依赖关系等问题，导致其在5G UDN下进行CDS预测的可靠性不足。本文提出采用一系列编码和采样方法，以定制化的细粒度层级对已知区域和未知区域的CDS进行建模。此外，我们设计了一个面向CDS热点预测的时空社交多特征提取框架，其中提出了一种新颖的边缘增强图卷积块，基于社交关系和空间特征对动态CDS网络进行编码。同时，引入长短期记忆网络（LSTM）进一步捕获时间依赖性。基于两个移动内容应用中采集的真实测量数据进行的广泛性能评估表明，我们提出的方案具有有效性，与最先进的方案相比，在76米空间粒度下可将预测的曲线下面积（AUC）提高40.5%，且对未知区域的预测覆盖率高达80%。