Fixed-priority Tasks Research Articles

Sharing Method of Online Teaching Resources of Spoken English Based on Deep Learning

In order to improve the sharing effect of online teaching resources of spoken English, this paper considers the sharing of resources among tasks in a mixed criticality system based on fixed task priority scheduling. Moreover, this paper extends the traditional PCP protocol and proposes a resource sharing protocol suitable for the AMC scheduling model in a verifiable hybrid criticality system. In addition, with the support of deep learning, this paper analyzes the worst blocking time and the worst response time of the task at each stage. Finally, this paper analyzes the sharing methods of online teaching resources of spoken English in combination with deep learning methods and constructs a corresponding intelligent teaching resource sharing system. The experimental research results show that the sharing method of online teaching resources of spoken English based on deep learning has a good resource sharing effect.

A Group-Based Energy-Efficient Dual Priority Scheduling for Real-Time Embedded Systems

As the limitation of energy consumption of real-time embedded systems becomes more and more strict, it has been difficult to ignore the time overhead and energy consumption of context switches for fixed-priority tasks with preemption scheduling (FPP) in multitasking environments. In addition, the scheduling for different types of tasks may disrupt each other and affect system reliability. A group-based energy-efficient dual priority scheduling (GEDP) is proposed in this paper. The GEDP isolates different types of tasks to avoid the disruption. Furthermore, it also reduces context switches effectively, thus decreasing system energy consumption. As many studies ignored the context switches’ overhead in the worst-case response time (WCRT) model, and it will affect the accuracy of WCRT, thereby affecting the system schedulability. Consequently, the WCRT model is improved based on considering context switches’ overhead. The GEDP is designed and implemented in Linux, and the time overhead and energy consumption of context switches is compared in different situations with GEDP and FPP. The experimental results show that GEDP can reduce context switches by about 1% and decrease energy consumption by about 0.6% for given tasks.

Fault-Tolerant Energy-Aware Task Scheduling on Multiprocessor System for Fixed-Priority Real-Time Tasks

Energy-aware real-time scheduling is gaining attention in recent years owing to environmental concerns and applications in numerous fields. System reliability also gets affected adversely with increasing energy dissipations posing serious challenges before the researchers. Keeping these in view, in recent times researchers have diverted to combining issues of fault-tolerance and energy efficiency. In literature, DVFS and DPM, most commonly used techniques for power management in task scheduling, are often combined with Primary/Backup technique to achieve fault tolerance against transient and permanent faults. Optimal algorithms, Earliest deadline first (EDF) and Rate-Monotonic (RM), meant for scheduling dynamic and fixed priority tasks respectively, have mainly been analyzed using a dual-processor approach for fault-tolerance and energy efficiency. In this paper, to handle higher workload of fixed-priority real-time tasks, energy-aware fault-tolerant scheduling algorithms are proposed for multiprocessor systems with balanced and unbalanced number of main and auxiliary processors. Simulations over extensive task-sets indicate that balanced approach is more energy-efficient than the unbalanced one.

Shared invariance control for constraint satisfaction in multi-robot systems

In systems involving multiple intelligent agents, e.g. multi-robot systems, the satisfaction of environmental, inter-agent, and task constraints is essential to ensure safe and successful task execution. This requires a constraint enforcing control scheme, which is able to allocate and distribute the required evasive control actions adequately among the agents, ideally according to the role of the agents or the importance of the executed tasks. In this work, we propose a shared invariance control scheme in combination with a suitable agent prioritization to control multiple agents safely and reliably. Based on the projection of the constraints into the input spaces of the individual agents using input–output linearization, shared invariance control determines constraint enforcing control inputs and facilitates implementation in a distributed manner. In order to allow for shared evasive actions, the control approach introduces weighting factors derived from a two-stage prioritization scheme, which allots the weights according to a variety of factors such as a fixed task priority, the number of constraints affecting each agent or a manipulability measure. The proposed control scheme is proven to guarantee constraint satisfaction. The approach is illustrated in simulations and an experimental evaluation on a dual-arm robotic platform.

Stretching algorithm for global scheduling of real-time DAG tasks

Parallelism is becoming more important nowadays due to the increasing use of multiprocessor systems. A Directed Acyclic Graph (DAG) is a general model of parallel tasks with inter-subtask parallelism. It consists of a collection of dependent subtasks under precedence constraints. In this paper, we study the problem of scheduling n periodic parallel real-time DAG tasks on m homogeneous multiprocessor systems. The dependencies between subtasks make scheduling process more challenging. We propose a stretching algorithm to be applied on each DAG task prior to scheduling process. Thus, DAGs are transformed into a set of independent sequential threads with intermediate offsets and deadlines. The threads obtained due to the transformation are of two types, (i) fully-stretched master threads with utilization equal to 1 and (ii) independent constrained-deadline threads. We propose a scheduling method over RTOS to ensure the execution of fully-stretched master threads on dedicated processors while the remaining processors $$\overline{m} \le m$$ , are used to schedule the independent constrained-deadline threads using any multiprocessor scheduling algorithm. In this work, we analyze the stretching algorithm while considering two global preemptive scheduling algorithms from different priority assignment families; the Global Earliest Deadline First (GEDF) from the fixed job priority family, and the Global Deadline Monotonic (GDM) from the fixed task priority family. We prove that GEDF scheduling of stretched threads has a resource augmentation bound equal to $$\frac{3+\sqrt{5}}{2}$$ for all task sets with $$n < \varphi \times \overline{m}$$ , where n is the number of tasks in the set and $$\varphi $$ is the golden ratio (the value of the golden ratio is $$\frac{1+\sqrt{5}}{2}$$ ). While GDM has a resource augmentation bound equal to $$2+\sqrt{3}$$ for all task sets with $$n < \frac{1+\sqrt{3}}{2} \times \overline{m}$$ .

Semi-partitioned scheduling for fixed-priority real-time tasks based on intelligent rate monotonic algorithm

In this paper, a new semi-partitioned scheduling algorithm on multiprocessor platforms, based on intelligent rate monotonic algorithm is proposed. Intelligent rate monotonic algorithm is an extended version of the famous rate monotonic algorithm. By splitting some tasks between processors, semi-partitioning is used to enhance overall utilisation. Each semi-partitioned approach has two phases, partitioning and scheduling. The main challenge of semi-partitioned scheduling algorithms is how to partition and split tasks by which they are safely scheduled under the identified scheduling policy, with high utilisation. The partitioning phase of our proposed approach called Semi-Partitioned Intelligent Rate Monotonic-First-Fit SIRM-FF includes three sub-phases. Task splitting is done only in the third sub-phase. In the second sub-phase, processors are selected by a first-fit method. The use of first-fit method makes SIRM-FF create a lower number of sub-tasks in comparison to previous works, hence the number of context switches of sub-tasks and overhead due to task splitting is reduced. The feasibility of tasks and sub-tasks which are partitioned by SIRM-FF is formally proved and overall utilisation is compared with competitors.

An SRP-based energy-efficient scheduling algorithm for dependent real-time tasks

In this paper, we are interested in the energy-efficient scheduling of dependent real-time tasks which may access multiunit resources. Based on the well-known stack resource policy (SRP), we propose the blocking-time stealing (BTS) algorithm to schedule dynamic-priority and fixed-priority tasks on a non-ideal dynamic voltage scaling (DVS) processor. Under the BTS algorithm, tasks are scheduled to be executed at proper processor speeds which are determined according to the sufficient schedulability conditions of the earliest deadline first (EDF) and the rate monotonic (RM) algorithms. The BTS algorithm measures dynamically the slack between the actual blocking time of a task and its estimated worst-case blocking time. The slack is then exploited to lower the processor speed guaranteeing that the feasibility of tasks is not affected. As a result, the energy consumption can be reduced while satisfying the timing constraints of real-time tasks.

Decentralized task scheduling for a fixed priority multicore embedded RTOS

Parallel task execution on multiple cores is increasingly the norm in high-performance embedded systems. Many multicore real-time operating systems (RTOSes) support a fixed task priority policy, and many applications that execute on such platforms require high performance while adhering to the fixed task priority policy. This paper proposes a decentralized task scheduling problem for a fixed-priority multicore RTOS running a mix of sequential and parallel tasks. Since aggressive decentralized task scheduling can result in task priority inversions, a high-performance priority-preserving solution to the proposed problem is challenging. To address this problem, 32 possible candidate solutions are considered and simulation studies are used to evaluate them. From extensive evaluations based on carefully prepared performance metrics, it is found that policies based on number of tasks, waiting time and priority (NTWP) for sequential tasks and small subtask first, then minimum number of tasks (MIN---MNT) for parallel tasks significantly outperform other possible candidates.

A unified analysis of global EDF and fixed-task-priority schedulability of sporadic task systems on multiprocessors

Fixed-task-priority (FTP) scheduling and earliest-deadline-first (EDF) scheduling policies are alike in fixing the priority of each job of a task at the time the job is released. This common feature of FTP and EDF scheduling permits a unified analysis of scheduling failures, to derive new sufficient tests for meeting all deadlines of a set of independent sporadic tasks under a global preemptive FTP or EDF scheduling policy. The performance of the new tests has been evaluated in comparison to prior schedulability tests, by simulation. The new tests are able to verify some schedulable task systems that could not be verified by prior tests, but also fail to verify some systems that can be verified by prior analysis techniques. The biggest gain appears to be for fixed-task-priority scheduling, especially with post-period deadlines.

Leakage-Aware Energy-Efficient Scheduling for Fixed-Priority Tasks with Preemption Thresholds

Leakage-Aware Energy-Efficient Scheduling for Fixed-Priority Tasks with Preemption Thresholds

Algorithm to Optimize Code Size and Energy Consumption in Real Time Embedded System

Processor is an important computing element in portable battery operated real time embedded system and it consumes most of the battery energy. Energy consumption, processor memory space are considered as basic design constraints in ARM based system and heuristic algorithm is developed for energy consumption as well as memory space management. This Algorithm includes rate monotonic fixed priority task scheduling scheme, DVS, ARM’s Normal /Thumb mode, execution time, and number of execution cycles, which are analytically related with energy consumption. Mathematical modeling and simulation of Heuristic algorithm is done using MATLAB. This gives optimized code size, execution time, energy consumption of each task / whole system and proves a novel strategy in the field of software related energy optimization and real time embedded system design.

A framework for the response time analysis of fixed-priority tasks with stochastic inter-arrival times

Real-time scheduling usually considers worst-case values for the parameters of task (or message stream) sets, in order to provide safe schedulability tests for hard real-time systems. However, worst-case conditions introduce a level of pessimism that is often inadequate for a certain class of (soft) real-time systems. In this paper we provide an approach for computing the stochastic response time of tasks where tasks have inter-arrival times described by discrete probabilistic distribution functions, instead of minimum inter-arrival (MIT) values.

Application-defined scheduling in Ada

This paper is a proposal for an application program interface (API) that would enable Ada applications to use application-defined scheduling algorithms in a way compatible with the scheduling model of the Ada 95 Real-Time Systems Annex. With this API, several application-defined schedulers, implemented by extending a tagged type, can coexist in the system in a predictable way together with their scheduled tasks, and with regular fixed priority tasks directly scheduled by the run-time system. Task synchronization through protected objects is also considered by adding the Stack Resource Policy, which can be used in a large variety of fixed and dynamic priority scheduling policies.

Software architecture for hard real-time applications: Cyclic executives vs. fixed priority executives

We contrast the software architecture of a hard real-time application using a fixed priority task structure against the software architecture of the same system using a cyclic executive structure to satisfy hard real-time deadlines in response to a set of embedded system requirements. We identify the perceived and actual advantages and disadvantages of both approaches, consider the types of applications which can take advantage of these approaches, and make recommendations related to the attributes of such applications that might be needed with both approaches. We conclude that the fixed priority approach, when priorities are assigned using rate monotonic priorities, generally dominates the cyclic executive approach for hard real-time systems.

Real-time UNIX operating system: RX-UX 832

This paper describes requirements for real-time UNIX operating systems, design concept and the implementation of RX-UX 832 real-time UNIX operating system for v60/v70 microprocessor which are NEC's 32-bit microprocessors. RX-UX 832 is implemented adopting the building block structure, composed of three modules, real-time kernel, file-server and UNIX supervisor. To guarantee a real-time responsibility, several enhancements were introduced such as, fixed priority task scheduling scheme, contiguous block file system and fault tolerant functions. Thus, RX-UX 832 allows system designers to use standard UNIX as its man-machine interface to build fault tolerant systems with sophisticated operability and provides high-quality software applications on the high performance microchips.