# A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
## 2 Related Work  
### Routing Algorithms in the Communication Network  
The Generalized Multi-Protocol Label Switching Virtual Private Networks/Path Computation Element (GMPLS/PCE) architecture is weak in the service recovery in case of link failure. [PERSON] in [10] introduced the SDN paradigm in the Elastic Optical Networks (EON). The logically centralized controller performed path computation and resource management operations and greatly shortened the recovery time. The authors in [11] presented a restoration strategy with a mechanism triggered in response to single-link failures in data centers over EONs. The proposed scheme exhibited the advantages of low-latencyrestoration, a high recovery ratio, etc. However, its performance should be further tested in practice. To achieve link protection and fault-independent path protection in the case of a single-link failure, [PERSON] [12] proposed several selection and evaluation methods for p-circles to improve the network survivability. In addition to link failures, IP router faults also impact the network survivability. To address this issue, the authors in [13] proposed a cost-efficient multilayer restoration method in IP-over-EONs in the case of a single-router outage. The authors formulated it as an ILP problem and proposed an auxiliary graph (AG)-based heuristic algorithm to improve the computational complexity. In case of link failure, [PERSON] [14] provided a rerouting algorithm that aimed to compute a path within the minimal recovery time in a set of the shortest path to distribute the packet on the failed link. On one hand, it restored the interrupted load, and on the other hand, it enabled an ability to distribute the traffic evenly in the network. In [15], a reinforcement learning-based routing optimization approach in the power grid is presented, which aims to minimize network delay and packet loss rates by optimizing link weights. As this method integrates graph neural networks with DRL frameworks, the optimization outcome may differ based on network topology. To ensure service QoS, the authors in [16] introduce a software-defined routing strategy by utilizing graph neural networks and a Q-learning algorithm to find optimal and sub-optimal routes for two distinct categories of control data packets, and the proposed approach was evaluated in the IEEE39 bus test system and the desired QoS routing could be achieved. To guarantee data transfer and resilience against a single-link failure, the authors in [17] designed a resilient and fault-tolerant routing algorithm in the PMU network based on the observability of the power grid, which minimized the end-to-end delay in the network, and the effectiveness was verified in the IEEE 14-bus system. Given the open and programmable characteristics of SDN, to satisfy differentiated QoS requirements of services in the power grid, the authors in [18] proposed a QoS-guarantee and congestion-controlled routing algorithm in the frame of SDN. A queuing model and the maximizing of a long-term network utility are constructed and the Lyapunov optimization theory is used to solve the optimization problem. The final routing decision was verified in a 10-node SDN network generated by Mininet. The end-to-end delay and packet loss rate were reduced. To realize a self-sustainable grid and real-time data sharing, the authors in [19] presented a cost-optimal model combination of technologies for a hybrid communication network in a smart distribution power grid. The authors comprehensively considered the actual cardinality of intelligent communications devices to satisfy their QoS metrics. A recursive algorithm was designed to solve this model and the simulations were performed on the IEEE 33 bus network. Aiming at the advanced metering infrastructure (AMI), a typical low power and lossy network featuring high packet loss and low transmission rate, the authors in [20] proposed a backup node selection mechanism to maximize the probability of successful data transmission.