Quantitative Schedulability Analysis of Continuous Probability Tasks in a Hierarchical Context

Abstract

We define the concept of degree of schedulability to characterize the schedulability and performance of soft real-time systems. The degree of schedulability of a system is given in terms of the two factors 1) Percentage of Missed Deadlines (PoMD); and 2) Degradation of the Quality of Service (DoQoS). Our work is set as a model-based framework for hierarchical scheduling systems where we introduce probability based sporadic tasks. The novel aspect is that we consider task arrival patterns that follow user-defined continuous probability distributions. The separately modeled task triggering events represent the system environment. We determine the degree of schedulability of a single scheduling component which can contain both periodic and sporadic tasks using statistical model checking in the form of Uppaal-SMC. Finally, we show the applicability of our framework by analyzing an avionics case study.