  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.
