# A Survey of Deep Learning for Data Caching in Edge Network
## 2 Data Caching Review
### Layer 1 Caching  
In Layer 1 Caching, the popular content is considered to be hosted in CRs. In the context of Information-Centric Network (ICN), CR plays dual roles both as a typical router (i.e., data flow forwarding) and content store (i.e., local area data caching facility). Generally, the CR is connected via wired networks. Layer 1 Caching consists of four tightly coupled problems: where to cache, what to cache, cache dimensioning, and content delivery [12]. Where to cache focuses on selecting the proper CRs to host the content. For instance, in Figure 2, contents replicas can be placed in lower hierarchical level CRs, such as router B and C, as the mean of reducing transmission cost but with extra cost pay for hosting contents; reversely, consolidating caching in CR A can be adopted to saving caching cost at the expense of more transmission cost and has the risk of expiring end users' delay requirement. Here, the caching/hosting cost is the cost to deploy the content, which could be measured by space utilization, energy consumption, or other metrics. The transmission cost represents the price for delivering the content from cached CR (or data server) to end user and is estimated via the number of hops generally. Where to cache problem usually has been modeled as a Mixed Integer Linear Programming (MILP):  
\[\min_{x}c^{T}x,\] (1a) s.t. \[Ax\leq b, \tag{1b}\] \[x\in\{0,1\},\] (1c) or \[x\geq 0, \tag{1d}\]  
where \(x\) is the decision variable. Normally it is a binary variable indicating the CR assignment. In special cases, with the aim of modeling or linearization, some non-binary auxiliary variables are introduced as constraint (1d) shows. If taking caching a part of a file not the complete into consideration, the decision variable \(x\) is a continuous variable representing the segments which are hosted in the CR, then constraint (1c) becomes \(x\in[0,1]\) and the MILP model turns to linear programming (LP). There are many papers allocating contents via MILP with different objectives and limitations. The authors in Reference [13] propose a model to minimize the user delay and load balancing level of CRs with the  
Figure 2: Data caching in edge network. satisfaction of cache space. The work in Reference [14] considers a trade-off between caching and transmission cost with cache space, link bandwidth, and user latency constraints. In Reference [15], an energy efficient optimization model is constructed consisting of caching energy and transport energy. Reference [16] provides more details of the mathematical model and related heuristic algorithms in caching deployment of wired networks. What to cache concentrates on selecting the proper contents in CRs for the purpose of maximizing the cache hit ratio. Via exploiting the statistical patterns of user requests, the popularity of requested information and user preference can be forecasted and play a very significant role in determining caching content. On the one hand, from the view of aggregated request contents, researchers propose many different models and algorithms for popularity estimation. One widely used model in web caching is the Zipf model based on the assumption that the content popularity is static and each users' request is independent [17]. However, this method fails to reflect the temporal and spatial correlations of the content, where the temporal correlation reflects the popularity varies over time and the spatial correlation represents the content preference is different on the geographical area and social cultural media. A temporal model named the shot noise model (SNM) is built in Reference [18] which enables users to estimate the content popularity dynamically. Inspired by SNM, the work in Reference [19] considers both spatial and temporal characteristics during caching decisions. On the other hand, from the view of a specific end user during a certain period, caching his/her preference content (may not be popular in the network) can also help to reduce the traffic flow.