Compositional feasibility analysis of conditional real-time task models

Conditional real-time task models, which are generalizations of periodic, sporadic, and multi-frame tasks, represent real world applications more accurately. These models can be classified based on a tradeoff in two dimensions - expressivity and hardness of schedulability analysis. In this work, we introduce a class of conditional task models and derive efficient schedulability analysis techniques for them. These models are more expressive than existing models for which efficient analysis techniques are known. In this work, we also lay the groundwork for schedulability analysis of hierarchical scheduling frameworks with conditional task models. We propose techniques that abstract timing requirements of conditional task models, and support compositional analysis using these abstractions.