We derive demand-bound functions for mixedcriticality sporadic tasks, and use these to determine EDFschedulability. Tasks have different demand-bound functions for each criticality mode. We show how to shift execution demand from high-to low-criticality mode by tuning the relative deadlines. This allows us to shape the demand characteristics of each task. We propose an efficient algorithm for tuning all relative deadlines of a task set in order to shape the total demand to the available supply of the computing platform. Experiments indicate that this approach is significantly more powerful than previous approaches to mixed-criticality scheduling. This new approach has the added benefit of supporting hierarchical scheduling frameworks.

We generalize the commonly used mixed-criticality sporadic task model to let all task parameters (execution-time, deadline and period) change between criticality modes. In addition, new tasks may be added in higher criticality modes and the modes may be arranged using any directed acyclic graph, where the nodes represent the different criticality modes and the edges the possible mode switches. We formulate demand bound functions for mixed-criticality sporadic tasks and use these to determine EDF-schedulability. Tasks have different demand bound functions for each criticality mode. We show how to shift execution demand between different criticality modes by tuning the relative deadlines. This allows us to shape the demand characteristics of each task. We propose efficient algorithms for tuning all relative deadlines of a task set in order to shape the total demand to the available supply of the computing platform. Experiments indicate that this approach is successful in practice. This new approach has the added benefit of supporting hierarchical scheduling frameworks.

Abstract-Models for real-time systems have to balance the inherently contradicting goals of expressiveness and analysis efficiency. Current task models with tractable feasibility tests have limited expressiveness, restricting their ability to model many systems accurately. In particular, they are all recurrent, preventing the modeling of structures like mode switches, local loops, etc.In this paper, we advance the state-of-the-art with a model that is free from these constraints. Our proposed task model is based on arbitrary directed graphs (digraphs) for job releases. We show that the feasibility problem on preemptive uniprocessors for our model remains tractable. This even holds in the case of task systems with arbitrary deadlines.

Abstract-An increasing trend in embedded system design is to integrate components with different levels of criticality into a shared hardware platform for better cost and power efficiency. Such mixed-criticality systems are subject to certifications at different levels of rigorousness, for validating the correctness of different subsystems on various confidence levels. The realtime scheduling of certifiable mixed-criticality systems has been recognized to be a challenging problem, where using traditional scheduling techniques may result in unacceptable resource waste. In this paper we present an algorithm called PLRS to schedule certifiable mixed-criticality sporadic tasks systems. PLRS uses fixed-job-priority scheduling, and assigns job priorities by exploring and balancing the asymmetric effects between the workload on different criticality levels. Comparing with the state-of-the-art algorithm by Li and Baruah for such systems, which we refer to as LB, PLRS is both more effective and more efficient: (i) The schedulability test of PLRS not only theoretically dominates, but also on average significantly outperforms LB's. (ii) The run-time complexity of PLRS is polynomial (quadratic in the number of tasks), which is much more efficient than the pseudo-polynomial run-time complexity of LB.

Abstract-In formal analysis of real-time systems, a major concern is the analysis efficiency. As the expressiveness of models grows, so grows the complexity of their analysis. A recently proposed model, the digraph real-time task model (DRT), offers high expressiveness well beyond traditional periodic task models. Still, the associated feasibility problem on preemptive uniprocessors remains tractable. It is an open question to what extent the expressiveness of the model can be further increased before the feasibility problem becomes intractable.In this paper, we study that tractability border. We show that system models with the need for global timing constraints make feasibility analysis intractable. However, our second technical result shows that it remains tractable if the number of global constraints is bounded by a constant. Thus, this paper establishes a precise borderline between tractability and intractability.