## 5 Performance Evaluation and Analysis
This is because _USNET_ has a higher average link connectivity compared with _NSFNET_, and more service carrying paths based on migration awareness can be provided, which can reduce BBR. Figure 5 shows that the bandwidth blocking ratio varies with different traffic load under different traffic models in _USNET_ topology by updating 16 nodes and _NSFNET_ topology updating 10 nodes. We can see that the trend of the bandwidth blocking ratio versus traffic load in _USNET_ topology and _NSFNET_ topology is similar. We can find that _BMA-RSA_ algorithm has a comparable performance with the _SP-RSA_ algorithm in terms of bandwidth blocking ratio. For example, in Figure 5a, at 620 Erlang traffic load, compared with the _SP-RSA_ algorithm, the bandwidth blocking ratio of the proposed _BMA-RSA_ algorithm has increased by 0%, 0%, 0.0012%, respectively, corresponding to K = 1, K = 2, K = 3. Likewise, at 780 Erlang traffic load shown in Figure 5b, the bandwidth blocking ratio of the _BMA-RSA_ algorithm has increased by 0.064%, 0.084%, 0.064% compared with the _SP-RSA_ algorithm. This is because the traditional _SP-RSA_ algorithm establishes the shortest path without considering the probability of node upgrade, and the shorter path means a lower bandwidth blocking ratio. However, the proposed _BMA-RSA_ algorithm fully considers the impact of node upgrade, in order to minimize the impact of a network upgrade on the services that have been carried, it may choose a relatively long path which \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traffic Demands (Gb/s)** & **Modulation Format** & **Operating Bandwidth (GHz)** & **Distance (km)** & **\# Slots** \\ \hline \multirow{4}{*}{40} & BPSK & 50 & 6000 & 4 \\ \cline{2-5}  & QPSK & 25 & 3000 & 2 \\ \cline{2-5}  & 8 QAM & 25 & 1000 & 1 \\ \hline \multirow{4}{*}{100} & BPSK & 75 & 4500 & 6 \\ \cline{2-5}  & QPSK & 50 & 3500 & 4 \\ \cline{2-5}  & QPSK & 37.5 & 3000 & 3 \\ \cline{2-5}  & 8 QAM & 25 & 2500 & 2 \\ \cline{2-5}  & 16 QAM & 25 & 1500 & 2 \\ \hline \multirow{4}{*}{200} & BPSK & 100 & 2500 & 8 \\ \cline{2-5}  & QPSK & 75 & 1500 & 6 \\ \cline{2-5}  & 8 QAM & 62.5 & 1000 & 5 \\ \cline{2-5}  & 16 QAM & 43.75 & 700 & 4 \\ \cline{2-5}  & 32 QAM & 37.5 & 500 & 3 \\ \hline \multirow{4}{*}{400} & BPSK & 200 & 2000 & 16 \\ \cline{2-5}  & QPSK & 150 & 1000 & 12 \\ \cline{2-5}  & 8 QAM & 100 & 800 & 8 \\ \cline{2-5}  & 16 QAM & 75 & 600 & 6 \\ \cline{2-5}  & 32 QAM & 56.25 & 200 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distance and Spectrum Occupation for Various Bit Rates in Flex-Grid. may not the optimal one. As a result, spectrum resources are not utilized optimally, which inevitably increases the bandwidth blocking ratio. The strategy under the uniform traffic model achieves better performance compared with the non-uniform traffic model. This is because that in the non-uniform case, traffic is unevenly distributed, which leads to a lower bandwidth blocking ratio. By comparing Figures 4 and 5, we reach the conclusion that upgrading 16 nodes and upgrading 10 nodes from fixed-grid to flex-grid nodes can lead to a lower bandwidth blocking ratio than can be achieved by upgrading 8 nodes and upgrading 5 nodes in _USNET_ and _NSFNET_, respectively. Because the super optical channel can only establish connections between flex-grid nodes, upgrading more nodes means more flexibility in the network. Figure 4: Simulation and results. (**a**) BBR under uniform traffic model when 8 nodes are upgraded in USNET; (**b**) BBR under non-uniform traffic model when 8 nodes are upgraded in USNET; (**c**) BBR under uniform traffic model when 5 nodes are upgraded in NSFNET; (**d**) BBR under non-uniform traffic model when 5 nodes are upgraded in NSFNET. Figures 6 and 7 show the connection interruption ratio for different traffic loads under uniform and non-uniform traffic models in the network of the _USNET_ topology with 8 nodes or 16 nodes upgraded and the _NSFNET_ topology with 5 nodes or 10 nodes upgraded.