Causal network telemetry

Current approaches to network observability rely on techniques like active probing, packet sampling, and path-level telemetry, which only provide a partial view. This paper presents causal telemetry, a new model that adapts ideas from distributed systems to the network setting. Causal telemetry captures causal relationships between events, including those that take place on physically separated devices. We motivate causal telemetry through examples, we show how it can be used to diagnose anomalies and faults, and we present algorithms for constructing the needed causal graphs from network executions. We develop a P4-based prototype implementation, CoCaTel, and discuss a case study that uses causal telemetry to detect Priority-Based Flow Control (PFC) deadlocks.