# A Survey of Deep Learning for Data Caching in Edge Network
## 2 Data Caching Review
### Layer 1 Caching
Many approaches in recommendation systems can be applied in this case [20]. Another aspect of what to cache problem is the designing of cache eviction strategies when storage space faces the risk of overflow. Depending on the life of caching contents, these policies can be divided into two categories roughly: one includes first in first out (FIFO), least frequently used (LFU), least recently used (LRU), and randomized replacement, where the contents would not be removed until no more memory is available; the other one is called time to live (TTL) strategy, where the eviction happens once the related timer expires. Reference [21] presents an analytic model for hit ratio in TTL-based cache requested by independent and identically distributed flows. It worth noting that in Reference [21], the TTL-based cache policy is used for the consistency of dynamic contents instead of contents replacement. In Reference [22], the authors introduce a TTL model for cache eviction and the timer is reset once related content cache hit happens. Cache dimensioning highlights how much storage space to be allocated. Benefitting from the softwarization and virtualization technologies, the cache size in each CR or edge cloud can be managed in a more flexible and dynamical way, which makes the cache dimensioning decisions become an important feature in data caching. Technically, the cache hit ratio rises with the increase of cache memory and consequently eases the traffic congestion in the core network. However, excessive space allocation would waste the resource, like energy, to support the caching function. Hence there is a trade-off between cache size cost and network congestion. Economically, taking such a scenario into consideration: a small content provider wants to rent service from a Content Delivery Network (CDN) provider, such as Akamai or Huawei Cloud, and there is also a balance between investment saving and network performance. In Reference [23], the proper cache size of individual CR in Content-Centric Network (CCN) is investigated via exploiting the network topology. In Reference [24], the authors consider the effect of network traffic distribution and user behaviors when designing cache size. Content delivery considers how to transform the caching content to the requested user. The delivery traffic embraces single cache file downloading and video content streaming and the metrics for these two scenarios vary. Regarding file downloading, the content cannot be consumed until the delivery is completed. Therefore, the downloading time of the entire file is viewed as a metric to reflect the quality of experience (QoE). For video streaming, especially for those large video split into several chunks, the delay limitation only works on the first chunk. In that case, delivering the first chunk in time and keep the smooth transmission of the rest chunks are the key aims [25]. Apart from those measuring metrics, another problem in content delivery is the routing policy. In CCN [26], one implementation of ICN architecture employs a flooding-based name routing protocol to publish the request among cached CRs. On one hand, flooding strategy simplifies the designing complexity and reduce the maintaining cost particularly in an unstable scenario; on the other hand, it costly wastes bandwidth resources. In Reference [27], the authors discuss the optimal radius in scoped flooding. The deliver route is often considered jointly with where to cache problem, in which the objective function (1a) includes both deployment and routing cost.