With the explosive demands for data, content delivery networks are facing ever-increasing challenges to meet end-users quality-of-experience requirements, especially in terms of delay. Content can be migrated from surrogate servers to local caches closer to end-users to address delay challenges. Unfortunately, these local caches have limited capacities, and when they are fully occupied, it may sometimes be necessary to remove their lower-priority content to accommodate higher-priority content. At other times, it may be necessary to return previously removed content to local caches. Downloading this content from surrogate servers is costly from the perspective of network usage, and potentially detrimental to the end-user QoE in terms of delay. In this paper, we consider an edge content delivery network with vehicular nodes and propose a content migration strategy in which local caches offload their contents to neighboring edge caches whenever feasible, instead of removing their contents when they are fully occupied. This process ensures that more contents remain in the vicinity of end-users. However, selecting which contents to migrate and to which neighboring cache to migrate is a complicated problem. This paper proposes a deep reinforcement learning approach to minimize the cost. Our simulation scenarios realized up to a 70% reduction of content access delay cost compared to conventional strategies with and without content migration.

翻译：随着数据需求的爆炸式增长，内容交付网络在满足终端用户服务质量体验要求方面面临日益严峻的挑战，尤其是时延方面。内容可从代理服务器迁移至更接近终端用户的本地缓存以应对时延挑战。然而，这些本地缓存容量有限，当其完全占满时，有时需要移除低优先级内容以容纳高优先级内容。另一些情况下，可能需要将此前移除的内容重新加载至本地缓存。从网络资源消耗角度看，从代理服务器下载此类内容代价高昂，且可能因时延问题对终端用户服务质量体验造成负面影响。本文考虑包含车载节点的边缘内容交付网络，提出一种内容迁移策略：当本地缓存被占满时，优先将内容卸载至相邻边缘缓存，而非直接移除。该过程确保更多内容保留在终端用户附近。然而，选择迁移哪些内容以及迁移至哪个相邻缓存是一个复杂问题。本文提出一种基于深度强化学习的方法以最小化代价。仿真实验表明，与包含及不包含内容迁移的传统策略相比，本文方案可实现内容访问时延代价最高降低70%。