## 3 Enhanced Name Resolution System
### Proactive Distribution of Name Binding Replicas  
Caching has been proved to be an effective way to reduce latency, to get fast look-up responses for local requests, as well as to improve the hit ratio [38; 43]. We considered using proactive name bindings replica placement and caching policies to leave multiple replicas of EID-NA bindings in the resolvers to improve the performance of ENRS. With more name record replicas, the number of nodes involved in the name resolution may be reduced, and with more name bindings duplicated to different hierarchies or the peer HM servers in the ENRS, the name resolution request can be responded to in a more agile manner. In addition, increasing the redundancy of name bindings can improve the fault-tolerance and resilience of ENRS. We prefer the off-path caching approach in restricting the name-binding replicas to be pre-deployed in ENRS because of its manageability and deployability. New copies of name bindings can be created by caching the name bindings of popular objects in ENRS's resolvers. In ENRS, we take both the locality and popularity of the names into account when placing the replicas of name bindings. A specific node called the Cache Manager (CM) is designated in each hierarchical HEA. It can be deployed in the HM of the same HEA, or deployed in a dedicated server at the same position of HM. CM is used to collect the request's information and some state information for EIDs in their domination areas. Significant factors, i.e., visited frequency, visited time of names, the geographical location distribution of users, and the distance between users and HMs, as well as user behavior information, e.g., moving speed and moving direction in mobility scenarios, are used to predict the replication factor K and the spreading scopes or transmission directions of name binding replicas for each EID or specific groups of EIDs, i.e., the EIDs produced by the same application, to ensure that users can get a name resolution response nearby. We call this mechanism Dynamic ENRS. Intelligent approaches, such as machine learning algorithms, can be used to predict the replicas' distribution of EIDs. We define Degree of Active(\(DoA\)) and Request Position Set(\(RPS\)) for EIDs also. DoA is comprised of parameters for EID's popularity and request characters. The RPS contains the user ID, whose visiting frequency of certain EIDs is larger than the threshold. The definitions of \(DoV\) and \(RPS\) are shown in Equation (1) and Equation (2). In Equation (1) and Equation (2), \(x\) represents EID, \(p(x)\) represents the popularity of \(x\), \(F_{u}(x)\) represents the update frequency of \(x\), \(F_{v}(x)\) represents the visited frequency of \(x\), \(F_{v}^{thres}\) is the threshold of \(F_{v}(x)\) to be added into \(RPS\). \(DoA\) will decide the \(K\) of each EID and \(RPS\) will decide the distribution of \(K\) replicas in hierarchy. \[DoA(x)=\alpha*p(x)+\beta*F_{u}(x)+r*F_{v}(x),\ \alpha>0,\beta>0,\gamma>0,\alpha+ \beta+\gamma=1 \tag{1}\]  
\[\forall F_{v}(x)\in RPS,F_{v}(x)\geq F_{v}^{thres} \tag{2}\]  
The diagram of Dynamic ENRS for name record replica diffusion in the tree topology of ENRS is shown in Figure 4. Differing from DMap [38], GMap [43], and Auspice [51] as being limited by the temporally hierarchical structure of ENRS, the pushing flow of the replicas is directional and must be pushed from the top-layer HMs to the bottom-layer HMs to satisfy the latency conditions, which are used when partitioning the hierarchical structure of HEAs. We use a flag to distinguish the source EID and replicated EID. The name binding replicas should also be registered in the BF. To reduce the overhead of HM, we could use collaborative caching approaches for name bindings within DNHT in one or multiple hops. What is more, we can use some location-aware techniques, such as the GPS of UE, to select the NAs that are closest to the users to get a fast look-up response for local requests.