# Optimization of Optical Networks Based on CDC-ROADM Technology
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Nodal Parameters** & **T1** & **T2** & **T3** & **M1** & **M2** & **WSS** \\ \hline bit rate [Gbps] & 10 & 40 & 100 & - & - & - \\ number of outputs & 1 & 1 & 1 & - & - & - \\ cost [cost units] & 1 & 2 & 5 & 1 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Node equipment parameters. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Polish** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
1 & & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 \\
2 & & & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 \\
3 & & & 7 & 7 & 7 & 7 & 7 & 7 & 75 Gbps to 500 Gbps, totaling up to maximum network capacity of 68 Tbps. Finally, in the case of the US network (Figure 7) the results are obtained for \(s\) values equal to 8, 16, 32, 64 and 96. 325 demands were considered and varied between 5 Gbps and 400 Gbps, totaling up to maximum network capacity of 130 Tbps. The summary of the maximum capacity calculated for each network is shown in Table 4.  
Comparing the results of C-ROADM-based and CDC-ROADM-based networks shown in Figures 4-7 shows that the DWDM network using C-ROADM technology becomes congested at much lower values of maximum capacity than the one using CDC-ROADM technology. In fact, CDC-ROADM can handle 2 to 3 times more traffic than C-ROADM-based network. Analyzing the results shown in Figures 4, 5, 6 and 7 leads to a conclusion that the costs of allocating demands within DWDM network based on C-ROADM technology are initially lower than in the case of CDC-ROADM technology. However, when C-ROADM-based DWDM network nears to its capacity limit the costs of its further expansion start to grow rapidly and eventually surpass those of CDC-ROADM-based network. Finally the applicability of integer programming as an optimization tool for considered DWDM networks is validated. In these simulations CPLEX solver execution time was terminated after 20 h (72,000 s) whether the optimal solution was reached or not. If the optimal solution was not found the CPLEX solver returned the best feasible solution and the lower bound. Table 5 shows the obtained values of objective function (network cost), computational time and the gap, i.e., the absolute value of the difference between the value of the network cost found using IP and that calculated using LP. The first six cases listed in Table 5 concern the link-path approach, and the last one concerns node-link \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **C-ROADM** & **CDC-ROADM** \\ \hline
**net5** & 2.5 Tbps & 8 Tbps \\
**Polish** & 30 Tbps & 66 Tbps \\
**German** & 32 Tbps & 68 Tbps \\
**US** & 43 Tbps & 130 Tbps \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of total network capacity for C-ROADM and CDC-ROADM, for tested networks. Figure 6: Dependence of maximum capacity of the network on the number of optical wavelength (**a**) and the dependence of network cost on maximum capacity of the network (**b**) for the German network. Figure 7: Dependence of maximum capacity of the network on the number of optical wavelength (**a**) and the dependence of network cost on maximum capacity of the network (**b**) for the US network. approach. Nodal parameters were taken from Table 2. The results from Table 5 show that CPLEX solver (IP) did not provide optimal solution but all calculated gaps were small (between 0.36% and 2.34%). Nonetheless, it is noted that the optimal solution was not achieved despite of a long computation time. The nearest to optimal solution was achieved using IP for the case which considers only one path in path-link approach, while the largest gap value was calculated for node-link approach. This is because the node-link approach is characterized by larger space of potential solutions and therefore, a long calculation time is required when using IP, otherwise heuristic methods can be applied to improve the computational efficiency.