# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
## 2 Related Work
### Name-Resolution Server Placement in Information-Centric Networking (ICN)  
As mentioned above, DLNR is an organizational framework for ICN name-resolution services in 5G-IoT scenarios. It brings two major constraints to the name-resolution server placement problem. Firstly, the name-resolution server is compulsive at providing a resolution service within an upper bound of latency. Secondly, name-resolution servers are organized as a multilayer to satisfy different requirements of name-resolution latency. Proper placement of the servers is critical to reduce the costs of system deployment. Therefore, we undertook a lot of research for inspiration. Proper placement of the servers is critical to reduce the costs of system deployment in multilayer overlay networks, such as the DLNR mentioned above. The server placement problem is a typical problem that is widely applied in cloudlet placement [34], controller deployment in software-defined networking [35], and virtual machine placement in cloud computing [36]. However, the server placement problem can usually be regarded as an NP-hard problem [37], so it has attracted the attention of researchers seeking to develop efficient algorithms to reach an approximate solution within a reasonable amount of time. [PERSON] et al. [38] studied how to place K cloudlets and allocate users to cloudlets in wireless metropolitan area networks (WMAN) such that the average system response time is minimized. They proposed a density-based clustering (DBC) algorithm to solve the problem with the aim of enabling the placement of cloudlets at regions with high user density. [PERSON] et al. [39] formulated a novel capacitated cloudlet placement problem that placed K cloudlets in different strategic locations. They showed that the problem is NP-hard and then devised a greedy-based heuristic algorithm to solve it. Since the greedy-based algorithm is simple and easy to implement to solve this problem, several k-means based algorithms have been presented [40; 41]. [PERSON] et al. [42] defined an edge-server problem as a multi-objective constraint optimization problem. They proposed an exact mixed-integer linear programming solution to minimize the total access delay between users and edge servers. Moreover, several heuristic algorithms were proposed to solve the server placement problem. In [43], [PERSON] et al. proposed a fast heuristic and a distributed genetic algorithm to minimize the maximum response time of all offloaded tasks. The authors in [44] proposed a new heuristic algorithm and a particle swarm optimization algorithm to find better server placement solutions. The studies mentioned above focused on the placement strategy with K servers. Their targets were to minimize the average access time and balance of offloading rather than minimizing the costs of servers. In [45], [PERSON] et al. placed cloudlets in the WMAN using a clustering algorithm, K-Medoids, with the objective of minimizing the count of cloudlets. [PERSON] and [PERSON] formulated the problem as a multi-objective optimization problem and devised a particle swarm optimization algorithm to find the optimal solution [46]. Studies in [47; 48] transformed the edge-server placement problem into an optimization problem that requires the dominating set of a given graph to be minimized, and graph algorithms were applied to solve the problem. Most of the studies on placement problems have focused on single-layer networks. When it comes to multilayer networks, the problem becomes even more complicated, because the simple superposition of optimal strategies in a single layer usually cannot obtain the optimal strategy in multi-layer networks. [PERSON] et al. [49] studied the hierarchical deployment of mobile edge computing servers and the user allocation problem. Their hierarchical deployment is relative to specific 5G entities, and no upper limit to access delay was considered. In [50], [PERSON] et al. studied placement problems in tree-like structures.