# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
## 1 Introduction
Emerging technologies such as fog computing [20] and edge computing [21,22] are advancing to enable 5G with ultra-reliable low-latency communication. In [23], [PERSON] et al. introduced the concept of deterministic latency into the NRS and proposed a deterministic latency name resolution (DLNR) framework. The DLNR treats a network as a multilayer overlay network using different latencies because [PERSON] et al. believe that different applications have diverse deterministic latency requirements. For each layer, network entities are partitioned into multiple areas based on the upper bound set by this layer. In addition, name-resolution servers in the DLNR are placed at the edge of the network to achieve lower latency and smaller latency jitter. Placing servers at the edge of the network is a promising solution that makes servers closer to end-users, providing a short latency response and high rate access [24]. However, this kind of approach implies a substantial increase in deployment and operational costs. Determining how to deploy servers to achieve a tradeoff between users' quality of service and deployment costs is a great challenge. In this paper, we study how to cost-effectively place name-resolution servers without violating the structural constraints of multilayer overlay networks in the DLNR. As far as we know, most previous research has focused on the server placement problem in a single layer network. Methods proposed in such studies do not work so well in multilayer overlay networks because these methods consider little about the coordination between layers. Thus, it is of great importance to investigate the server placement problem in multilayer overlay networks. The main contributions of this paper are as follows:  
* We model the problem of latency-bounded optimal server placement in multilayer overlay networks and formulate it as an integer linear program problem with the objective of minimizing the deployment costs;
* We develop two algorithms based on the heuristic ideas of inter-layer information transfer (IIT) and server reuse. The IIT-DOWN algorithm passes the server placement information from the high-level layer to the low-level layer. It reuses servers chosen in the high-level layer to provide low-level services as well. The IIT-UP algorithm passes the server placement information as well as detailed latency information from low to high. The network scale shrinks during this procedure, and the execution time reduces greatly;
* We conduct experiments on different scales of simulation networks and a real-world dataset to measure the performance of our algorithms. We compare our algorithms with several approaches to solve the server placement problem, and the experimental results show that our algorithms can find more cost-efficient solutions with a shorter execution time. The remainder of this paper is organized as follows. We review the related work about name-resolution systems and the server placement problem in Section 2. Section 3 presents the system model and problem statement of the name-resolution server placement in multilayer overlay networks. In Section 4, two algorithms are described in detail; then, we evaluate and discuss their performance with a comparison in Section 5. Finally, Section 6 concludes our work.