# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 2 Background and Motivations
### Caching Strategies in the Literature  
To improve the content retrieval performance, several NDN caching strategies have been proposed in the last few years [2]. Among them, we can identify two major categories, namely popularity-based schemes and centrality-based schemes, which leverage, respectively, content popularity information and network topology information to select which contents to cache and where. In popularity-based schemes [10; 11; 12; 13; 14], NDN nodes cache the most popular contents according to the locally measured rate of received Interests, which is stored in a Popularity Table. Typically, a threshold based mechanism is considered: contents that are requested a number of times higher than the threshold are cached. In centrality-based schemes [4], instead, the caching decision depends on the _importance_ of the nodes, expressed in terms of topological centrality. For instance, the pioneering proposal called _Betw_[4] leverages the betweenness centrality of the nodes as decision metric and replaces contents with the least recently used (LRU) policy. According to the graph theory, given a set \(V\) of network nodes, the betweenness centrality (\(C_{B}\)) of a node \(v_{i}\in V\) is defined as:  
\[C_{B}(v_{i})=\sum_{v_{k}\
eq v_{j}\
eq v_{i}\in V}\frac{\sigma_{v_{k},v_{j}}(v _{i})}{\sigma_{v_{k},v_{j}}}, \tag{1}\]  
with \(\sigma_{v_{k},v_{j}}(v_{i})\) and \(\sigma_{v_{k},v_{j}}\) being, respectively, the number of shortest content delivery paths from the two endpoints \(v_{k}\) and \(v_{j}\) that pass through \(v_{i}\) and the total number of shortest content delivery paths between the same endpoints. _Betw_ strategy is built on the following intuition: if a node \(v_{i}\) is traversed by many content delivery paths, then it is more likely to get a cache hit. Therefore, contents are cached in the nodes with the higher betweenness centrality. In [5], however, the authors observe that the topological centrality metric alone does not reflect the importance of a node from the content delivery perspective. Therefore, the authors define a new popularity-aware centrality metric, which aims at placing the most popular contents at high central nodes, and the remaining contents with decreasing popularity at nodes with decreasing centrality score. With the same target, the Betweenness and Edge Popularity caching (BEP) strategy [6] leverages a coordinated signalling mechanism piggybacked into Interest and Data packets. In BEP, the edge nodes (i.e., the leaf nodes directly connected to the consumers) track the number of received requests and periodically compute the content popularity with an exponential weighted moving average (EWMA) formula. The information is maintained in a Popularity Table, where contents are also ranked in terms of popularity. When a content request arrives, the edge node includes the correspondent popularity ranking in the Interest and forwards it towards the origin source. In addition, the Interest carries an array of betweenness centrality values, which is filled by all on-path routers. When the origin source receives the Interest, it compares the popularity ranking against the available betweenees values and identifies the cache by matching the two metrics, i.e., if the content has the highest popularity, it will be cached in the node with the highest centrality, and so on. Despite their differences, all the noted approaches consider the betweenness centrality in the caching decision. However, this metric does not guarantee the minimum retrieval delay for the consumers. Indeed, especially when considering edge domains, typically characterized by hierarchical topologies [8], the nodes with the highest betweenness centrality are usually far away from the leaf nodes, where the consumers are attached. Instead, a peripheral edge node, e.g., a base station (BS), has typically low betweenness centrality, but it can be very close to the consumers and cover a key role as cacher.