###### Abstract  
Research has been conducted to efficiently transfer blocks and reduce network costs when decoding and recovering data from an erasure coding-based distributed file system. Technologies using software-defined network (SDN) controllers can collect and more efficiently manage network data. However, the bandwidth depends dynamically on the number of data transmitted on the network, and the data transfer time is inefficient owing to the longer latency of existing routing paths when nodes and switches fail. We propose deep Q-network erasure coding (DQN-EC) to solve routing problems by converging erasure coding with DQN to learn dynamically changing network elements. Using the SDN controller, DQN-EC collects the status, number, and block size of nodes possessing stored blocks during erasure coding. The fat-tree network topology used for experimental evaluation collects elements of typical network packets, the bandwidth of the nodes and switches, and other information. The data collected undergo deep reinforcement learning to avoid node and switch failures and provide optimized routing paths by selecting switches that efficiently conduct block transfers. DQN-EC achieves a 2.5-times-faster block transmission time and 0.4-times-higher network throughput than open shortest path first (OSPF) routing algorithms. The bottleneck bandwidth and transmission link cost can be reduced, improving the recovery time approximately twofold. deep reinforcement learning; network routing; deep q-network; erasure coding; distributed clustering systems +
Footnote †: journal: Medical  
a network delay. To address this problem, continuous research has been conducted to optimize the network routing paths. Typically, we establish a server that can aggregate the data and reduce the number of transmission links required for transferring blocks while addressing network bottlenecks [5; 6]. However, such studies have not focused on detecting dynamic bandwidths that occasionally change in actual network topologies. In a real-world network topology, the bandwidth is transmitted by multiple packets to manage the health of each server, as well as the block transfers related to erasure coding. SDN is a new network technology that separates the control of switches and routers within a network from the data area [7]. SDN controllers allow for the central control of the entire network and network management. In addition, SDN controllers have a global view of the entire network, allowing comprehensive network information to be collected. However, although data can be collected through an SDN for efficient network management, efficient packet delivery remains a challenge in terms of network throughput and latency. Therefore, optimizing network resources requires deploying and resolving routing paths that are optimized for the network. The optimized routing path problem is how to efficiently forward data traffic from the source node to all reachable destination nodes and switches, and to find routing paths to destination nodes that conduct decoding operations. Traditionally used routing path algorithms in networks include OSPF [8]. However, OSPF cannot predict network changes because it does not consider dynamic changes in bandwidth based on the shortest paths. In other words, the optimal routing path cannot be found in a network with a dynamically changing bandwidth and traffic distribution. In summary, in an erasure coding-based distributed file system, the network routing methodology has difficulty identifying real-time changes in the network bandwidth and failed nodes because it does not take real data into account. In addition, efficient network routing paths are difficult to create because supervised learning-based network routing methodologies do not consider data from real networks. Although reinforcement-based network routing can generate efficient routing paths compared to supervised learning, it is difficult to apply the model's suitability and network environment.