Arrival- and service-curve estimation methods from measurements to analyze and design soft real-time streaming systems with network calculus

 This paper surveys different arrival and service curve estimation methods for basic linear functions from experimental data. We experiment with methods we found in literature and discuss the benefit and deficit of these methods. We propose two own developed simple methods inspired by literature, to estimate rate-latency service curves matching to our use case to analyze a soft real-time streaming system with Network Calculus. We furthermore apply three methods to a novel field for performance evaluation with network calculus: hardware-in-the-loop test benches. The performance evaluation of these test systems and the two estimation methods are our contribution to the field of network calculus applicated to soft real-time streaming systems. The performance metric playback buffer and the minimum needed pre-buffer time are dimensioned with the help of adapted and mathematically proven network calculus solutions for streaming devices from our last paper. Linear network calculus elements are generated in three different ways based on measurements of software processing latencies and network latencies. In the next step, playback buffer backlog and delay bounds are calculated based upon the three service curve estimation methods by applying network calculus to the measurements. These calculated bounds are compared to each other and discussed and furthermore verified by a discrete-event simulation model with trace-driven input and with distribution fits of the processing latencies. The simulation model uses a variation of the workload to prove the system, while being operated in soft real-time operating point, with the mean arrival-rate near the mean service-rate and above the minimum service-rate. We recommend using the delay metrics by NC with a safety-factor to design the pre-buffer time.