# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
###### Abstract
designed rounding algorithms with bounded approximation factors to address the faulty-link recovery problem; the SDN controller pre-computed alternative paths before the link failure occurred, enabling fast recovery [10]. [PERSON] et al. proposed a backup path search algorithm that considered both backup path length and link load balancing, which reduced the backup path length and the maximum link utilization during fault recovery [11]. [PERSON] et al. introduced a path risk metric to re-route the end-to-end traffic of a failed link by minimizing traffic operations, thereby ensuring fast recovery of the traffic [12]. [PERSON] et al. proposed the Hybrid Fast Path Recovery Algorithm (HFPR-A); when a communication link fails, HFPR-A quickly identifies the shortest path or an approximate shortest path between nodes using the fast path recovery algorithm [13]. The control plane-based faulty-link recovery algorithm mentioned above first locates the position of the faulty link in the network topology and then designs a recovery algorithm that satisfies constraints such as the shortest recovery path and link load. Finally, the SDN controller calculates new forwarding paths for the network topology and sends the updated flow table entries to the switches. However, these methods introduce significant latency during the interaction between the controller and the switches, making them unsuitable for WAMSs, where end-to-end data transmission latency is tightly constrained. 2. Data Plane-Based Approach for Fast Recovery of Faulty Links  
The data plane-based approach for recovery of faulty links requires the participation of the controller during the recovery process. After detecting a link failure, the controller calculates new data forwarding paths and configures the forwarding rules in the switches. In reference [14], a ring-based single-link failure recovery method (RSFR) was proposed, where backup paths were designed based on node importance and link performance within the network topology. These paths were periodically updated to reduce the consumption of backup resources. In reference [15], backup paths were pre-stored in the switches. When a link failure occurred, the disrupted data packets were aggregated into a single flow entry using the VLAN ID, thereby reducing the number of traffic packets. In reference [16], the objective of minimizing the disrupted traffic caused by link failures was considered. This was achieved by formulating the selection of backup paths as an integer programming problem, which reduced the need for backup flow rules and conserved backup bandwidth. The FFRLI fault-link recovery algorithm was proposed in reference [17] and used Markov chains to assess the importance of links. It preinstalled backup paths for primary routes within switches, while storing backup paths for secondary links in the controller, thereby effectively reducing the consumption of backup resources. Based on the mathematical analysis of WAMS topology characteristics, reference [18] proposed the (1+2\({}_{\ell}\))-BPCA backup path construction algorithm and backup path installation method, which effectively reduced data transmission delays during and after failover. The data plane-based fault recovery method mentioned above addresses the prolonged recovery time issue associated with control plane-based fault recovery by pre-embedding the backup path in the switch. However, the pre-calculated backup paths do not fully account for link load conditions. As a result, when a link fails, the data being routed through the backup path are vulnerable to secondary link failures due to congestion within the backup path. To address the aforementioned issues, this paper proposes a fast fault recovery method for communication links in WAMS networks, based on the data plane of SDN. The method, named Load Balancing Backup Path Based on Deep Deterministic Policy Gradient (DDPG-LBBP), leverages deep reinforcement learning for efficient fault recovery.