In recent years, extensive research has been carried out on real-time typed scheduling and analysis of parallel tasks represented as directed acyclic graphs (DAGs) executed on heterogeneous multicores. Although previous studies have examined the schedulability of typed DAG tasks, they still encounter pessimism caused by interference from other tasks. In this article, we explore the worst case response time (WCRT) analysis of typed scheduling for DAG tasks under global scheduling. Here, each vertex in a typed DAG task experiences interference from within itself as well as from higher priority tasks. First, we propose an efficient method to bound the WCRT of typed DAG tasks based on the state-of-the-art parallel task analysis approach. Then, we discover a technique to mitigate the pessimism caused by other tasks, albeit in a nonoptimal manner. Finally, we conduct experiments using randomly generated typed DAG tasks to evaluate the performance of our proposed methods. The results indicate that our proposed approach can yield less pessimistic WCRT under global scheduling.

IEEE 802.1Qch, also known as cyclic queuing and forwarding (CQF) shaper, enhanced the dynamic and flexible behavior of time-sensitive networking. Schedulability of the CQF is one of the major research fields used to improve system performance. Existing CQF heuristic scheduling approaches only consider the relative deadline in stream sorting or the link utilization in route selection, which encounters limitations while striving to attain high schedulability of streams. We propose an efficient scheduling for CQF. The main contributions are: We provide the earliest virtual absolute deadline first policy, which prioritizes stream instances based on their earliest absolute deadlines, intending to prior allocate resources to the stream instance with the greatest “urgency.” To provide a deep insight into efficient route selection, we then introduce a quality-of-service-aware indicator of a route that mainly accounts for slot utilization and route length, which enables us to select a route with a balance of slot utilization and shorter route length for a stream instance. With the comprehensive evaluation, our method achieves an average schedulability improvement of 31.73%, 17.93%, and 31.85% compared with state-of-the-art methods. Furthermore, our approach can be extended to other CQF-related shapers or time-division scheduling scenarios, significantly enhancing stream schedulability.

Time-Sensitive Networking (TSN) has become the most popular technique in modern safety-critical Automotive and Industrial Automation Networks by providing deterministic transmission policies. However, the data of TSN messages may be affected by transient faults. IEEE 802.1CB, a reliability standard in TSN, protects against such faults by providing disjoint redundant routes for each stream. However, the unique assumption may present a new challenge, i.e., an inadequate number of redundant routes that may negatively impact stream scheduling. This paper presents an offline fault-tolerant TSN scheduling approach that considers such impacts for real-time streams (such as Time-Trigger (TT) and Audio Video Bridging (AVB) streams). Specifically, we intend to calculate the minimum upper bound number of disjoint routes required for each stream to meet the reliability requirements, subsequently enhancing the network’s schedulability. We also propose a service degradation function for AVB streams when the network is under heavy load caused by redundant transmissions of TT streams. This function will maintain schedulability and reliability for AVB streams. Experiments with small-and large-scale synthetic networks show the efficiency.

In recent years, the maturation of modern multicore processor technology and its increasing adoption in critical industrial domains have posed significant challenges for real-time systems, primarily due to contention for shared cache resources and the resulting uncertainty. To address this issue, contemporary processors employ cache partitioning techniques, enhancing temporal predictability by isolating cache access among processor cores. However, this isolation technique may lead to real-time tasks missing their deadlines due to an insufficient number of cache partitions. Consequently, this paper investigates the schedulability of preemptive global Earliest Deadline First (EDF) real-time scheduling algorithms that support dynamic cache allocation. We propose an innovative LAG-based schedulability analysis method for these algorithms and present a utilization-based schedulability condition that reduces analysis time complexity while improving analysis accuracy. Lastly, the performance and efficiency of the proposed schedulability determination method are validated through simulation experiments with randomly generated tasks.

With the increasing demand for real-time computing applications on energy-harvesting embedded devices which are deployed wherever it is not possible or practical to recharge, the worst-case performance analysis becomes crucial. However, it is difficult to bound the worst-case response time of tasks under both timing and energy constraints due to the uncertainty of harvested energy. Based on this motivation, this paper studies response time analysis for Energy-Harvesting Mixed-Criticality (EHMC) systems. We present schedulability analysis algorithm to extend the Adaptive Mixed Criticality (AMC) approach to EHMC systems. Furthermore, we develop two response time bounds for it. To our best knowledge, this is the first work of response time analysis for EHMC systems. Finally, we examine both the effectiveness and the tightness of the bounds by experiments.

This paper presents CaM, a holistic cache and memory bandwidth resource allocation strategy for multicore real-time systems. CaM is designed for partitioned scheduling, where tasks are mapped onto cores, and the shared cache and memory bandwidth resources are partitioned among cores to reduce resource interferences due to concurrent accesses. Based on our extension of LITMUSRT with Intel's Cache Allocation Technology and MemGuard, we present an experimental evaluation of the relationship between the allocation of cache and memory bandwidth resources and a task's WCET. Our resource allocation strategy exploits this relationship to map tasks onto cores, and to compute the resource allocation for each core. By grouping tasks with similar characteristics (in terms of resource demands) to the same core, it enables tasks on each core to fully utilize the assigned resources. In addition, based on the tasks' execution time behaviors with respect to their assigned resources, we can determine a desirable allocation that maximizes schedulability under resource constraints. Extensive evaluations using real-world benchmarks show that CaM offers near optimal schedulability performance while being highly efficient, and that it substantially outperforms existing solutions.