In this paper, we study the scheduling problem of the imprecise mixed-criticality model (IMC) under earliest deadline first with virtual deadline (EDF-VD) scheduling upon uniprocessor systems. Two schedulability tests are presented. The first test is a concise utilization-based test which can be applied to the implicit deadline IMC task set. The suboptimality of the proposed utilization-based test is evaluated via a widely-used scheduling metric, speedup factors. The second test is a more effective test but with higher complexity which is based on the concept of demand bound function (DBF). The proposed DBF-based test is more generic and can apply to constrained deadline IMC task set. Moreover, in order to address the high time cost of the existing deadline tuning algorithm, we propose a novel algorithm which significantly improve the efficiency of the deadline tuning procedure. Experimental results show the effectiveness of our proposed schedulability tests, confirm the theoretical suboptimality results with respect to speedup factor, and demonstrate the efficiency of our proposed algorithm over the existing deadline tunning algorithm. In addition, issues related to the implementation of the IMC model under EDF-VD are discussed.

In this paper, we investigate the problem of using the state-of-the-art C=D task-splitting approach to energy efficiently schedule real-time tasks on a single-ISA heterogeneous multicore system. We first extend the existing task-splitting approach for heterogeneous multicore systems. Based on our extension, we propose an algorithm, called ASHM, to allocate and split realtime tasks on a heterogeneous multicore system. The experimental results demonstrate the effectiveness of our proposed ASHM algorithm compared to existing allocation approaches in terms of energy savings.

This paper studies real-time scheduling of mixed-criticality systems where low-criticality tasks are still guaranteed some service in the high-criticality mode, with reduced execution budgets. First, we present a utilization-based schedulability test for such systems under EDF-VD scheduling. Second, we quantify the suboptimality of EDF-VD (with our test condition) in terms of speedup factors. In general, the speedup factor is a function with respect to the ratio between the amount of resource required by different types of tasks in different criticality modes, and reaches 4/3 in the worst case. Furthermore, we show that the proposed utilization-based schedulability test and speedup factor results apply to the elastic mixed-criticality model as well. Experiments show effectiveness of our proposed method and confirm the theoretical suboptimality results.

Recently, it has been shown that the hard real-time scheduling theory can be applied to streaming applications modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. However, this recent approach is not always efficient in terms of throughput and processor utilization. Therefore, in this article, we propose an improved hard real-time scheduling approach to schedule streaming applications modeled as acyclic CSDF graphs on a Multiprocessor System-on-Chip (MPSoC) platform. The proposed approach converts each actor in a CSDF graph to a set of real-time periodic tasks. The conversion enables application of many hard real-time scheduling algorithms that offer fast calculation of the required number of processors for scheduling the tasks. In addition, we propose a method to reduce the graph latency when the converted tasks are scheduled as real-time periodic tasks. We evaluate the performance and time complexity of our approach in comparison to several existing scheduling approaches. Experiments on a set of real-life streaming applications demonstrate that our approach (1) results in systems with higher throughput and better processor utilization in comparison to the existing hard real-time scheduling approach for CSDF graphs, while requiring comparable time for the system derivation; (2) delivers shorter application latency by applying the proposed method for graph latency reduction while providing better throughput and processor utilization when compared to the existing hard real-time scheduling approach; (3) gives the same throughput as the existing periodic scheduling approach for CSDF graphs, but requires much shorter time to derive the task schedule and tasks’ parameters (periods, start times, and so on); and (4) gives the throughput that is equal to or very close to the maximum achievable throughput of an application obtained via self-timed scheduling, but requires much shorter time to derive the schedule. The total time needed for the proposed conversion approach and the calculation of the minimum number of processors needed to schedule the tasks and the calculation of the size of communication buffers between tasks is in the range of seconds.

In this paper, we study the problem of minimizing the number of processors required for scheduling latency-constrained streaming applications modeled as CSDF graphs, where the actors of a CSDF are executed as strictly periodic tasks. We formalize the problem and prove that due to the strict periodicity of actors the problem is an integer convex programming problem, that can be solved efficiently by using an existing convex programming solver. We evaluate our solution approach on a set of 13 real-life streaming applications modeled as CSDF graphs and demonstrate that it can reduce the number of processors in more than 52% of the conducted experiments in comparison to an existing approach.