# A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
## 4 Solution Approach
### Path Satisfaction-Based Service-Aware Routing Algorithm (PSSRA) for the Energy Internet
To test the performance of the PSSRA in the large-scale network, we compared it with some other typical approaches: the Integer Linear Programming (ILP), which provides the optimal solutions (e.g., time-minimized and cost-minimized), the shortest path algorithm, and the maximal path TSC (SP-MPT), and the Reinforcement learning Routing Algorithm (RLRA) [44] and GA-MLWDF in [7]. Here, the first two are the optimal solutions with the expense of intensive computations and time-consuming attributes, and the last two heuristic algorithms can explore near-optimal solutions. The results of SBRs are shown in Figure 3:8 It demonstrates that the results of the PSSRA approximate to the ILP and are superior to the AI-based approaches with the increase in services in the 28-node network, which shows the effectiveness of the PSSRA. Figure 3:9 Computation time in the 14-node network. To test the algorithm performance, we conducted and compared it with other algorithms in this network topology. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**SBR**} & \multicolumn{8}{c}{**Service Request Number (\(\times 10^{4}\))**} \\ \cline{2-7}  & **2** & **4** & **6** & **8** & **10** & **12** & **14** \\ \hline
7-Node network & 0.0003 & 0.0163 & 0.0359 & 0.0638 & 0.0882 & 0.0935 & 0.1191 \\
14-Node network & 0.0053 & 0.0152 & 0.0233 & 0.0326 & 0.0372 & 0.0435 & 0.0491 \\
28-Node network & 0.0117 & 0.0134 & 0.0247 & 0.0307 & 0.0514 & 0.0601 & 0.0916 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The SBR with the PSSRA in different network topologies. ## 5 Conclusions

In this paper, different routing and rerouting algorithms in the backbone Optical Transport Network are first investigated. To facilitate the centralized service deployment and execution efficiency, a collaborative network structure-based SDN was designed. To satisfy differentiated performance requirements, services were classified into different levels in terms of time sensitivity and the importance of stable operation in the EI. To search for appropriate routing path for these services, a path optimization model based on link satisfaction by considering link availability, SRLG, and link spectrum connectivity was established. A heuristic algorithm was designed to improve algorithm performance. The simulation results demonstrated that the approach achieved a lower service blocking ratio (SBR) and a higher Jair's fairness index (JFI) compared with other approaches. In our future work, we will conduct further studies on the real-world network to validate the effectiveness of the PSSRA and explore more effective routing algorithms in the Energy Internet through some advanced machine learning algorithms. X.W. : Conceptualization, methodology; H.H. : writing--review and editing; L.S. : creation of models; P.Y. : formal analysis; B.L. : writing--original draft. All authors have read and agreed to the published version of the manuscript. This research was funded by the Pingdingshan University Doctoral Foundation (Grant No. PXY-BSQD-2022004), Open Foundation of State key Laboratory of Networking and Switching Technology (Grant No. SKLNST-2022-1-10), Pingdingshan University students' Innovation and Entrepreneurship Training plan for 2024 (Grant No. 109192025052, 109192025068), the Key scientific research projects of colleges and universities in Henan Province of China (25A520040), and the Scientific and Technological Project in Henan Province of China (Grant No. 242102210205, 242102210131). All of the data or codes generated during the study are available from the corresponding authors by request. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. ## References

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