CollaSFC: An Intelligent Collaborative Approach for In-network SFC Failure Detection in Data Center for AI Computing

The successful application cases of Large Language Models (LLMs) and Machine Learning (ML) are driving traditional data centers to transform into intelligent computing data centers characterized by low latency, high bandwidth, high reliability, and zero packet loss. The demand for immense computing and ultra-low latency suggests that in-network computing (INC) may be a viable solution, such as In-network aggregation (INA). INA involves a hierarchical structure of switches and servers to form different Service Function Chains (SFCs) including switches, servers, physical links, and virtual links for accomplishing model training. However, the aggregation of heavy traffic in CTCs tends to a sudden and drastic increase in a specific node, greatly increasing the likelihood of node failure. To detect SFC failure in real time, we propose an in-network SFC failure detection approach based on INC. We introduce digital twins (DT) and propose a collaborative AI framework based on the data plane and control plane to avoid model overfitting. In addition, to reduce the computing consumption, we propose the concept of "multiple SFC chains multiple models" to customize each SFC failure detection model and validate the mechanism on a BMv2-based prototype, which implements a high-accuracy failure detection with minor performance degradation.