# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
## 4 Proposed Algorithms
### IIT-UP Algorithm
This algorithm passes the server placement information from the high-level layer to the low-level layer. Servers chosen in the high-level layer are reused to provide low-level services as well. The second one was called IIT-UP, and it follows the same idea but passes information, including both server location and detailed latency information in the opposite direction. Experiments were conducted on both simulation networks and a real-world dataset. The results demonstrated that our proposed IIT algorithms outperform other existing algorithms in terms of finding more cost-efficient solutions within a shorter execution time. The IIT-DOWN algorithm has the advantage of reducing the number of high-level layer servers. However, IIT-UP can shrink the network scale during its procedure and it greatly reduces the execution. In addition, during the study, we also discovered two facts. One was that the size of the servers grew linearly with the network size in multilayer overlay networks if the physical network was in line with the BA scale-free model. The other one was that average latencies at each layer remained stable while the network size increased. Future work should focus on two aspects. Firstly, the selection of the set of upper bounds of latencies has a significant influence on the solution of the problem. We only selected the corresponding upper bounds in some typical scenarios, which cannot represent all the scenarios. More upper-bound latency combinations should be evaluated and analyzed to discover the potential rule. Secondly, because name resolution plays an essential role in ICN, having a robust name-resolution service is meaningful. Redundant overlay areas exist during the partition procedure in our proposed algorithms. Further study should aim to make the best of these redundancies to improve the robustness of the name-resolution service. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Execution Time (s)**} & \multicolumn{4}{c}{**Level**} & \multirow{2}{*}{**Cost**} \\ \cline{3-4} \cline{5-6}  & & **3 (0.2 ms)** & & **2 (0.05 ms)** & **1 (0.01 ms)** \\ \hline LHP & 79.49 & 5 & 34 & 302 & 308 \\ Random Allocate (RA) & 101.74 & 6 & 42 & 328 & 342 \\ Minimum Dominator Set in Multilayer (MDSM) & 27.31 & 4 & 27 & 286 & 286 \\ Inter-layer Information Transfer Down (IIT-DOWN) & 28.98 & 4 & 27 & 283 & 283 \\ Inter-layer Information Transfer Up (IIT-UP) & 13.57 & 4 & 37 & 267 & 267 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of algorithms on Shanghai Telecom’s base stations dataset. Author Contributions:Conceptualization, J.L., Y.S., and H.D. ; methodology, J.L., Y.S. ; software, J.L., writing--original draft preparation, J.L. ; writing--review and editing, J.L., Y.S., and H.D. ; supervision, Y.S. ; project administration, Y.S. ; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript. This work was funded by Strategic Leadership Project of Chinese Academy of Sciences: SEANET Technology Standardization Research System Development (Project No. XDC02070100). We would like to express our gratitude to [PERSON], [PERSON], and [PERSON] for their meaningful support for this work. The authors declare no conflict of interest. ## References

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