Mixed-critical systems consist of applications with different criticality. In these systems, different confidence levels of Worst-Case Execution Time (WCET) estimations are used. Dual criticality systems use a less pessimistic, but with lower level of assurance, WCET estimation, and a safe, but pessimistic, WCET estimation. Initially, both high and low criticality tasks are executed. When a high criticality task exceeds its less pessimistic WCET, the system switches mode and low criticality tasks are usually dropped, reducing the overall system Quality of Service (QoS). To postpone mode switch, and thus, improve QoS, existing approaches explore the slack, created dynamically, when the actual execution of a task is faster than its WCET. However, existing approaches observe this slack only after the task has finished execution. To enhance dynamic slack exploitation, we propose a fine-grained approach that is able to expose the slack during the progress of a task, and safely uses it to postpone mode switch. The evaluation results show that the proposed approach has lower cost and achieves significant improvements in avoiding mode-switch, compared to existing approaches.

Over-approximated Worst-Case Execution Time (WCET) estimations for multi-cores lead to safe, but over-provisioned, systems and underutilized cores. To reduce WCET pessimism, interference-sensitive WCET (isWCET) estimations are used. Although they provide tighter WCET bounds, they are valid only for a specific schedule solution. Existing approaches have to maintain this isWCET schedule solution at run-time, via time-triggered execution, in order to be safe. Hence, any earlier execution of tasks, enabled by adapting the isWCET schedule solution, is not possible. In this paper, we present a dynamic approach that safely adapts isWCET schedules during execution, by relaxing or completely removing isWCET schedule dependencies, depending on the progress of each core. In this way, an earlier task execution is enabled, creating time slack that can be used by safety-critical and mixed-criticality systems to provide higher Quality-of-Services or execute other best-effort applications. The Response-Time Analysis (RTA) of the proposed approach is presented, showing that although the approach is dynamic, it is fully predictable with bounded WCET. To support our contribution, we evaluate the behavior and the scalability of the proposed approach for different application types and execution configurations on the 8-core Texas Instruments TMS320C6678 platform, obtaining significant performance improvements compared to static approaches.

Mixed-critical systems consist of high criticality and low criticality applications. When a high criticality task exceeds its less pessimistic Worst Case Execution Time (WCET), the system switches mode and low criticality tasks are usually dropped. To postpone mode switch, existing approaches explore the slack, created dynamically due to an actual execution of a task that is faster than its WCET. However, the slack is visible only after the task has finished. To further enable dynamic slack exploitation, we propose a fine-grained approach that exposes the slack created due to the progress of tasks, during execution, and safely uses it to postpone mode switch.

Multi-core systems using ScratchPad Memories (SPMs) are attractive architectures for executing time-critical embedded applications, because they provide both predictability and performance. In this paper, we propose a scheduling technique that jointly selects SPM contents off-line, in such a way that the cost of SPM loading/unloading is hidden. Communications are fragmented to augment hiding possibilities. Experimental results show the effectiveness of the proposed technique on streaming applications and synthetic task-graphs. The overlapping of communications with computations allows the length of generated schedules to be reduced by 4% on average on streaming applications, with a maximum of 16%, and by 8% on average for synthetic task graphs. We further show on a case study that generated schedules can be implemented with low overhead on a predictable multi-core architecture (Kalray MPPA).

Static Worst-Case Execution Time (WCET) estimation techniques operate upon the binary code of a program in order to provide the necessary input for schedulability analysis techniques. Compilers used to generate this binary code include tens of optimizations, that can radically change the flow information of the program. Such information is hard to be maintained across optimization passes and may render automatic extraction of important flow information, such as loop bounds, impossible. Thus, compiler optimizations, especially the sophisticated optimizations of mainstream compilers, are typically avoided. In this work, we explore for the first time iterative-compilation techniques that reconcile compiler optimizations and static WCET estimation. We propose a novel learning technique that selects sequences of optimizations that minimize the WCET estimate of a given program. We experimentally evaluate the proposed technique using an industrial WCET estimation tool (AbsInt aiT) over a set of 46 benchmarks from four different benchmarks suites, including reference WCET benchmark applications, image processing kernels and telecommunication applications. Experimental results show that WCET estimates are reduced on average by 20.3% using the proposed technique, as compared to the best compiler optimization level applicable.

In time-critical systems, run-time adaptation is required to improve the performance of time-triggered execution, derived based on Worst-Case Execution Time (WCET) of tasks. By improving performance, the systems can provide higher Quality-of-Service, in safety-critical systems, or execute other best-effort applications, in mixed-critical systems. To achieve this goal, we propose a parallel interference-sensitive run-time adaptation mechanism that enables a fine-grained synchronisation among cores. Since the run-time adaptation of offline solutions can potentially violate the timing guarantees, we present the Response-Time Analysis (RTA) of the proposed mechanism showing that the system execution is free of timing-anomalies. The RTA takes into account the timing behavior of the proposed mechanism and its associated WCET. To support our contribution, we evaluate the behavior and the scalability of the proposed approach for different application types and execution configurations on the 8-core Texas Instruments TMS320C6678 platform. The obtained results show significant performance improvement compared to state-of-the-art centralized approaches.

Safe and optimal deployment of data-streaming applications on many-core platforms requires the realistic estimation of task Worst-Case Execution Time (WCET). On the other hand, task WCET depends on the deployment solution, due to the varying number of interferences on shared resources, thus introducing a cyclic dependency. Moreover, WCET is still an over-approximation of the Actual Execution Time (AET), thus leaving room for run-time optimisation. In this paper we introduce an offline/online optimisation approach. In the offline phase, we first break the cyclic dependency and acquire safe and near-optimal solutions for tasks partitioning/placement, mapping, scheduling and buffer allocation. Then, we tighten the WCETs and update the scheduling function accordingly. In the online phase we introduce a safe distributed readjustment of the offline schedule, based on the AET. Experiments on a Kalray MPPA-256 platform show a tightening of the guaranteed latency up to 46% in the offline phase, and 41% latency reduction in the online phase. In total, we achieve more than 50% of latency reduction.