Convolutional Neural Network-Based Data Distribution Routing Framework for Service Function Chaining for ROS-Based Industrial Automation

With continuous advancements in robotics and networking, the adoption of smart industrial machines for automation has grown significantly. Leveraging the Robot Operating System version 2 (ROS-2) and its topic-based data distribution mechanism, these machines are typically controlled by software deployed in the cloud. This control software is structured as a chain of ROS-2 nodes, with each node responsible for managing a specific function or component of the industrial machine. By applying the concept of Service Function Chaining (SFC), these ROS-2 node chains can be deployed in a distributed manner across cloud infrastructure. An SDN controller is employed to steer ROS-2 traffic efficiently along the sequence of nodes. As the number of industrial machines increases in large-scale factories, the number of corresponding ROS-2 node chains also grows. This trend underscores the need to reuse common ROS-2 nodes across multiple chains to optimize the utilization of cloud resources. However, the default topic-based communication model of ROS-2 is not inherently compatible with such reuse. Sharing common ROS-2 nodes across different chains becomes challenging, thereby hindering efficient resource management in cloud environments. To address this limitation, this paper proposes a Data Distribution Routing (DDR) framework powered by a Spatial Attention Convolutional Neural Network (SA-CNN). The DDR is capable of classifying and steering ROS-2 traffic originating from shared nodes to their appropriate service chains. Furthermore, it supports the NetConf protocol to communicate with the Software-Defined Networking (SDN) controller. Our evaluation includes both neural network performance and system-level metrics. The SA-CNN outperforms Vanilla CNN and CNN+LSTM in classification accuracy and efficiency. Additionally, the DDR shows favorable resource usage, latency, and throughput under real-world workloads.