This article proposes a novel optimization technique of fault-tolerant mixed-criticality multi-core systems with worst-case response time (WCRT) guarantees. Typically, in fault-tolerant multi-core systems, tasks can be replicated or re-executed in order to enhance the reliability. In addition, based on the policy of mixed-criticality scheduling, low-criticality tasks can be dropped at runtime. Such uncertainties caused by hardening and mixed-criticality scheduling make WCRT analysis very difficult. We show that previous analysis techniques are pessimistic as they consider avoidably extreme cases that can be safely ignored within the given reliability constraint. We improve the analysis in order to tighten the pessimism of WCRT estimates by considering the maximum number of faults to be tolerated. Further, we improve the mixed-criticality scheduling by allowing partial dropping of low-criticality tasks. On top of those, we explore the design space of hardening, task-to-core mapping, and quality-of-service of the multi-core mixed-criticality systems. The effectiveness of the proposed technique is verified by extensive experiments with synthetic and real-life benchmarks.

An engine management system consists of periodic or sporadic real-time tasks. A task is a set of runnables that may be fully preemptive or partially at runnable boundaries. A cause-effect chain is defined as a chain of runnables that are connected by the read/write dependency. We propose a novel analytical technique to estimate the end-to-end latency of a cause-effect chain by considering conservatively estimated schedule time bounds of associated runnables. The proposed approach is verified with an industrial-strength automotive benchmark.

In this article, we propose a novel technique that estimates a tight upper bound of the worst-case response time (WCRT) of a synchronous dataflow (SDF) graph when the SDF graph shares processors with other real-time tasks. When an SDF graph is executed at runtime under a self-timed or static assignment scheduling policy on a multi-processor system, static scheduling of the SDF graph does not guarantee the satisfaction of latency constraints since changes to the schedule may result in timing anomalies. To estimate the WCRT of an SDF graph with a given mapping and scheduling result, we first construct a task instance dependency graph that depicts the dependency between node executions in a static schedule. The proposed technique combines two techniques in a novel way: schedule time bound analysis and response time analysis. The former is used to consider the interference between task instances in the same SDF graph, and the latter is used to consider the interference from other real-time tasks. Through extensive experiments with synthetic examples and benchmarks, we verify the superior performance of the proposed technique compared to other existent techniques.

In this paper, we propose a novel analytical method, called scheduling time bound analysis, to find a tight upper bound of the worst-case response time in a distributed real-time embedded system, considering execution time variations of tasks, jitter of input arrivals, and scheduling anomaly behavior in a multi-tasking system all together. By analyzing the graph topology and worst-case scheduling scenarios, we measure the conservative scheduling time bound of each task. The proposed method supports an arbitrary mixture of preemptive and non-preemptive processing elements. Its speed is comparable to compositional approaches while it gives a much tighter bound. The advantages of the proposed approach compared with related work were verified by experimental results with randomly generated task graphs and a real-life automotive application.

In this paper we are concerned about executing synchronous dataflow (SDF) applications on a multicore architecture where a core has a limited size of scratchpad memory (SPM). Unlike traditional multi-processor scheduling of SDF graphs, we consider the SPM size limitation that incurs code and data overlay overhead. Since the scheduling problem is intractable, we propose an EA(evolutionary algorithm)-based technique. To hide memory latency, prefetching is aggressively performed in the proposed technique. The experimental results show that our approach reduces the overlay overhead significantly compared to a non-optimized approach and the previous approach.

Finding a tight upper bound of the worst-case response time in a distributed real-time embedded system is a very challenging problem since we have to consider execution time variations of tasks, jitter of input arrivals, scheduling anomaly behavior in a multi-tasking system, all together. In this paper, we translate the problem as an optimization problem and propose a novel solution based on ILP (Integer Linear Programming). In the proposed technique, we formulate a set of ILP formulas in a compositional way for modeling flexibility, but solve the problem holistically to achieve tighter upper bounds. To mitigate the time complexity of the ILP method, we perform static analysis based on a scheduling heuristic to reduce the number of variables and confine the variable ranges. Preliminary experiments with the benchmarks used in the related work and a real-life example show promising results that give tight bounds in an affordable solution time.