  Real-Time Networks (RTNs) provide latency guarantees for time-critical
applications and it aims to support different traffic categories via various
scheduling mechanisms. Those scheduling mechanisms rely on a precise network
performance measurement to dynamically adjust the scheduling strategies.
Machine Learning (ML) offers an iterative procedure to measure network
performance. Network Calculus (NC) can calculate the bounds for the main
performance indexes such as latencies and throughputs in an RTN for ML. Thus,
the ML and NC integration improve overall calculation efficiency. This paper
will provide a survey for different approaches of Real-Time Network performance
measurement via NC as well as ML and present their results, dependencies, and
application scenarios.
