MPulse: A Programmable and Autonomic Fault Detection System via Hierarchical Liveness Exchange

Modern data center networks underpin latency-critical services such as cloud computing, AI workloads and emerging 6G applications, yet even brief switch or link failures can cascade into severe service disruptions. Existing fault detection systems face limitations: control plane centric methods incur high latency (seconds to minutes) and suffer control channel overload, while data-plane-centric approaches often rely on intrusive probing, limiting scalability. We present MPulse, a multi-dimensional fault detection and recovery framework. Our primary idea is to differentiate the roles of monitoring switches and transmission switches, shifting fault management to the data plane through two key innovations: collaborative multidimensional monitoring and a decentralized voting mechanism. Following this, we enable switches to collaboratively collect real-time status, historical data, and topological state through periodic information exchange, allowing each node to aggregate global multi-dimensional insights. Then, a distributed voting process is designed to allow switches to independently determine fault types (link/switch failure) and locations based on integrated data dimensions, eliminating dependencies on centralized control. Implemented on Intel Tofino switches with minimal overhead, MPulse achieves sub-millisecond fault localization (<1 ms), 324.8× faster detection than existing solutions, and near-100% accuracy under congestion. Experimental results validate its effectiveness and low operational costs.