Modern microservice architectures produce observability data in many shapes and forms, spread across physically and logically separate parts of the infrastructure. This makes it hard to pinpoint what went wrong when failures cascade through the system.
The preprint, titled "Semantic Observability for Autonomous Systems Diagnostics," argues that a semantic layer could help make sense of this heterogeneous log data. The abstract specifically highlights the goal of isolating root causes during cascading failures, suggesting the authors see semantics as a way to cut through the noise.
However, the abstract is brief and does not detail the proposed method or any experimental results. Readers will need to consult the full paper to learn how semantic observability is implemented and whether it delivers on its promise.