Minimizing Stack Memory for Partitioned Mixed-criticality Scheduling on Multiprocessor Platforms

Along with the prevailing of mobile devices, the demand for efficient power consumption has become one of the major issues in designing embedded system. Dynamic Voltage Scaling (DVS) is a technique that can reduce energy consumption by changing the processor voltage levels dynamically. Fixed Priority with Preemption Threshold (FPPT) scheduling is a scheduling policy that includes preemptive and non-preemptive aspect of scheduling policy. In this paper, an efficient universal fixed priority DVS algorithm (FPPT-DVS) will be presented. This algorithm has the advantage of both Fixed Priority Preemptive (FPP) DVS scheduling and Fixed Priority Non-Preemptive (FPNP) DVS scheduling. FPPT-DVS algorithm also combines the on-line DVS and off-line DVS together. Experimental results show that the proposed FPPT-DVS algorithm can save up to 20% energy over the existing DVS algorithms.

Memory is a limited resource in cost-sensitive, resource-constrained embedded applications. Preemption Threshold Scheduling (PTS) is a well-known technique for reducing the system stack size requirement. We consider Global Fixed Priority Scheduling with Preemption Threshold (gFPPT), as integration of PTS with global Fixed-Priority scheduling on a homogeneous multiprocessor platform, and formulate the optimization problem of minimizing the system stack size requirement while guaranteeing schedulability. We present schedulability analysis, optimization algorithms for priority and preemption threshold assignment, and an ILP formulation for computing system stack size requirement. Performance evaluation shows that the system stack size requirement can be reduced significantly with gFPPT compared to preemptive scheduling.

Fixed priority scheduling is an important real-time scheduling scheme widely used in practice. To improve the schedulability of fixed priority scheduling considerable effort has been made such as introduction of preemption threshold or deferred preemption, and quantum-based scheduling. In this paper, we develop a new scheduling scheme by introducing both preemption threshold and quantum into one scheduling framework. The new scheduling method may successfully schedule tasks which are not schedulable either by preemption threshold scheduling or quantum-based scheduling, as well as tasks schedulable by either scheduling, thus improves the schedulability of fixed-priority tasks. We analyze the schedulability of the new scheduler by computing the worst case response time of tasks. Based on the analysis, we developed an algorithm for assignment of preemption threshold and quantum sizes.

Multicore processors are increasingly adopted in resource-constrained real-time embedded applications. In the development of such applications, efficient use of RAM memory is as important as the effective scheduling of software tasks. Preemption Threshold Scheduling (PTS) is a well-known technique for controlling the degree of preemption, possibly improving system schedulability, and to reduce system stack usage. In this paper, we consider partitioned multi-processor scheduling on a multicore processor with either Fixed-Priority or Earliest Deadline First scheduling algorithms with PTS and address the design optimization problem of mapping tasks to processor cores and assignment of task priorities and preemption thresholds with the optimization objective of minimizing system stack usage. We present both optimal solution techniques based on Mixed Integer Linear Programming and efficient heuristic algorithms that can achieve high-quality results. We perform extensive performance evaluations using both synthetic tasksets and industrial case studies.

Multicore platforms are increasingly used in realtime embedded applications. In the development of such applications, an efficient use of RAM memory is as important as the effective scheduling of software tasks. Preemption Threshold Scheduling is a well-known technique for controlling the degree of preemption, possibly improving system schedulability, and allowing savings in stack space. In this paper, we target at the optimal mapping of tasks to cores and the assignment of the scheduling parameters for systems scheduled with preemption thresholds. We formulate the optimization problems using Mixed Integer Linear Programming framework, and propose an efficient heuristic as an alternative. We demonstrate the efficiency and quality of both approaches with extensive experiments using random systems as well as two industrial case studies.

Mixed-Criticality Scheduling (MCS) is an effective approach to addressing diverse certification requirements of safety-critical systems that integrate multiple subsystems with different levels of criticality. Preemption Threshold Scheduling (PTS) is a well-known technique for controlling the degree of preemption, ranging from fully-preemptive to fully-non-preemptive scheduling. We present schedulability analysis algorithms to enable integration of PTS with MCS, in order to bring the rich benefits of PTS into MCS, including minimizing the application stack space requirement, reducing the number of runtime task preemptions, and improving schedulability.

Multicore platforms are increasingly used in real-time embedded applications. In the development of such applications, an efficient use of RAM memory is as important as the effective scheduling of software tasks. Preemption Threshold Scheduling is a well-known technique for controlling the degree of preemption, possibly improving system schedulability, and allowing savings in stack space. In this paper, we target at the optimal mapping of tasks to cores and the assignment of the scheduling parameters for systems scheduled with preemption thresholds. We formulate the optimization problems using Mixed Integer Linear Programming framework, and propose an efficient heuristic as an alternative. We demonstrate the efficiency and quality of both approaches with extensive experiments using random systems as well as two industrial case studies.

Fixed-priority preemption-threshold scheduling (FPTS) is a generalization of fixed-priority preemptive scheduling (FPPS) and fixed-priority non-preemptive scheduling (FPNS). Since FPPS and FPNS are incomparable in terms of potential schedulability, FPTS has the advantage that it can schedule any task set schedulable by FPPS or FPNS and some that are not schedulable by either. FPTS is based on the idea that each task is assigned a priority and a preemption threshold. While tasks are admitted into the system according to their priorities, they can only be preempted by tasks that have priority higher than the preemption threshold. This paper presents a new optimal priority and preemption threshold assignment (OPTA) algorithm for FPTS which in general outperforms the existing algorithms in terms of the size of the explored state-space and the total number of worst case response time calculations performed. The algorithm is based on back-tracking, i.e. it traverses the space of potential priorities and preemption thresholds, while pruning infeasible paths, and returns the first assignment deemed schedulable. We present the evaluation results where we compare the complexity of the new algorithm with the existing one. We show that the new algorithm significantly reduces the time needed to find a solution. Through a comparative evaluation, we show the improvements that can be achieved in terms of schedulability ratio by our OPTA compared to a deadline monotonic priority assignment.

In the last decade, the rapid evolution of the Commercial-Off-The-Shelf (COTS) platforms led safety-critical systems towards integrating tasks and applications with different criticality levels in a shared hardware platform, i.e., Mixed-Criticality Systems (MCS)s. Therefore, several scheduling algorithms and approaches have been proposed upon a commonly used model, i.e., Vestal's model. However, consolidating software functions onto shared processors cannot be implemented directly in real-life applications and industrial systems while complying with certification requirements. The existing scheduling approaches do not provide a simple solution for eliminating the interference effect among the tasks with different criticality levels on the shared processing resources. Moreover, the system mode switch guarantees the timing constraints of the high-criticality tasks throw the termination of the low-criticality tasks. In this paper, we developed a new scheduling algorithm that addresses these challenges based on the round-robin technique, which improves the overall schedulability. We compared the proposed algorithm against existing scheduling algorithms in both academia and industry using extensive experiments to evaluate it. Our results show improvements in the schedulability from 0.8% to 14.0% and from 2.7% to 10.7% compared to the conventional Earliest Deadline First with Virtual Deadline (EDF-VD) and Fixed Priority Preemptive (FPP) scheduling approaches, respectively.

Fixed priority schedulers are widely used for real-time systems, and there were efforts to improve the schedulability. For example, preemption threshold scheduling increases the schedulability by introducing non-preemptiveness during task execution. In this paper, we study another scheduling model that can improve the schedulability of tasks: quantum-based scheduling. When quantum-based scheduling is combined with priority-based scheduling, the quantum introduces a partially non-preemptive region which causes a priority inversion. We show that the non-preemptiveness introduced by quantum-based scheduling can improve the schedulability of fixed priority tasks, and there is a task set which is not schedulable by preemption threshold scheduling but is schedulable by quantum-based scheduling. This paper also presents a method to calculate the worst case response time of tasks in quantum-based scheduling.

In a mixed-criticality system, multiple tasks with different levels of criticality may coexist on the same hardware platform. The scheduling algorithm EDF-VD (Earliest Deadline First with Virtual Deadlines) has been proposed for mixed-criticality systems, which assumes tasks do not share any common resources. We present MC-SRP (Mixed-Criticality Stack Resource Policy), a resource synchronization protocol for EDF-VD, which allows resource sharing among tasks at the same criticality level and guarantees that each task is blocked at most once in each criticality mode. In addition, we present MC-SRPT (MC-SRP with Thresholds) for reducing the application stack size requirement in resource-constrained embedded systems.

For preemptive scheduling with shared cache, different tasks may cause interference in the shared cache, leading to Cache-Related Preemption Overhead (CRPD). Cache partitioning is a well-known technique for mitigating unpredictable cache interference in preemptive scheduling, but it reduces cache space available to each task, causing an increase in task execution time. Non-preemptive scheduling algorithms do not incur CRPD, but they generally have poor schedulability. Preemption Threshold Scheduling (PTS) is an effective approach to strike a balance between preemptive and non-preemptive scheduling. We propose integration of cache partitioning and PTS to optimize schedulability on a uniprocessor. We force each subset of tasks assigned the same cache partition to be a non-preemptive group, by assigning the same PT to all tasks in the subset that is equal to or higher than the highest priority of the tasks in that subset. This eliminates CRPD within each cache partition, and helps to improve schedulability. We present an ILP formulation as well as an efficient heuristic algorithm.

Multiprocessor hardware platforms are now being considered for embedded systems, due to their high computational power and little additional cost when compared to single processor systems. When scheduling real-time applications on multiprocessor platforms, a possibility is to use global scheduling, where a scheduling algorithm dynamically assign tasks to processors, and tasks can migrate from one processor to another during their execution. In this paper, we tackle the problem of schedulability analysis of sporadic tasks in global scheduling systems, where the scheduler is the earliest deadline first (EDF) algorithm. We provide two main contributions. First, we show that two recently proposed tests perform poorly when the task set contains heavy tasks (i.e. tasks with high utilization). We also show that neither test dominates the other. As a second contribution, we introduce a new schedulability test that improves significantly the percentage of accepted task sets, especially when considering task sets containing heavy tasks. We show the effectiveness of the proposed test through an extensive set of experiments.

Mixed criticality systems are one of the relatively new directions of development for the classical real-time systems. As the real-time embedded systems become more and more complex, incorporating different tasks with different criticality levels, the continuous development of mixed criticality systems is only natural. These systems have practically entered every field where embedded systems are present: avionics, automotive, medical systems, wearable devices, home automation, industry and even the Internet of Things. While scheduling techniques have already been proposed in the literature for different types of mixed criticality systems, the number of papers addressing multiprocessor platforms running in a time-triggered mixed criticality environment is relatively low. These algorithms are easier to certify due to their complete determinism and isolation between components of different criticalities. Our research has centered on the problem of real-time scheduling on multiprocessor platforms for periodic tasks in a time-triggered mixed criticality environment. A partitioned, non-preemptive, table-driven scheduling algorithm was proposed, called Partitioned Time-Triggered Own Criticality Based Priority, based on a uniprocessor mixed criticality method. Furthermore, an analysis of the scheduling algorithm is provided in terms of success ratio by comparing it against an event-driven and a time-triggered method.

Motivated by the increasing trend in embedded systems towards platform integration, there has been an increasing research interest in scheduling mixed-criticality systems. However, most existing efforts have concentrated on scheduling sequential tasks and ignored intra-task parallelism. In this paper, we study the scheduling of mixed-criticality parallel jobs on multiprocessor platforms. We propose a synchronous mixed-criticality job model, where each job consists of segments, each segment having an arbitrary number of parallel threads that synchronize at the end of the segment. A novel MinLoad algorithm is developed to decompose mixed-criticality parallel jobs into mixed-criticality sequential jobs. This decomposition enables us to leverage existing mixed-criticality scheduling algorithms and schedulability analysis to the multiprocessor scheduling of mixed-criticality parallel jobs. In addition, our MinLoad job decomposition algorithm is designed to make the decomposed mixed-criticality sequential tasks easier to schedule, and thus requires smaller-sized multiprocessor platforms for the mixed-criticality systems.