Fault-tolerant Scheduling Of Real-time Tasks Research Articles

Research on Scientific Research Data Platform Based on Big Data

In order to realize the fault tolerance of real-time tasks in scientific research data platform information platform, based on adaptive feedback equalization and symbol modulation technology, a fault-tolerant scheduling model of real-time tasks in scientific research data platform information is proposed in the paper. What is more, the platform transmission channel model of the scientific research data platform information under routing conflicts is first constructed to optimize the information transmission protocol of the scientific research data platform. Secondly, the fuzzy C-means clustering method is applied to perform the inspection robot information fusion. Meanwhile, the symbol modulation method is used for fault-tolerant scheduling of real-time tasks in the information platform. Finally, a simulation experiment is carried out. The results show that the method proposed in the paper has better fault tolerance for platform real-time task scheduling of scientific research data platform information, and the channel balance of the information platform is stronger, which improves the platform real-time task scheduling capability of scientific research data platform information.

Resource-Aware Scheduling for Dependable Multicore Real-Time Systems: Utilization Bound and Partitioning Algorithm

As the computing devices and software executions are susceptible to manifold faults, fault tolerance has been an important research topic in safety-critical real-time systems. Moreover, multicore processors have recently emerged as prevailing computing engines for modern embedded systems. However, there exists rather rare work on the fault-tolerant scheduling of real-time tasks executing on multicores with shared resources, where the task synchronization originated from resource access contention may significantly degrade the schedulability of task system. With the focus on the partitioned-EDF scheduler with the MSRP (Multiprocessor Stack Resource Policy) protocol and primary/backup recovery mechanism, we first investigate a utilization bound and then identify its anomaly where the bound may decrease when more cores are deployed. Next, following the insights gained by the analysis of the bound, we propose a reliability and synchronization aware task partitioning algorithm (RSA-TPA) together with an efficient version to implement the joint management of task synchronization and system reliability, where several resource-oriented heuristics are developed to improve both the schedulability performance and workload balancing. The extensive simulation results show that the RSA-TPA schemes can obtain higher acceptance ratio (e.g., 60 percent more) and generate more balanced partitions, when compared to the existing schemes that consider either reliability management or task synchronization. Finally, with the different fault arrival rates being considered, the actual implementation in Linux kernel further demonstrates the applicability of RSA-TPA that has lower run-time overhead (e.g., 20 percent less) in comparison with other mapping algorithms.

DFTS: A dynamic fault-tolerant scheduling for real-time tasks in multicore processors

This paper presents a dynamic scheduling for real-time tasks in multicore processors to tolerate single and multiple transient faults. The scheduling is performed based on three important issues: (1) current released tasks, (2) current available processor cores, and (3) consideration of the number of faults and their occurrences. Using tasks utilization along with a defined criticality threshold in the proposed scheduling method, current ready tasks are divided into critical- and noncritical ones. Based on whether a task is critical or noncritical, an appropriate fault-tolerance policy is exploited. Moreover, scheduling decisions are made to fulfill two key goals: (1) increasing scheduling feasibility and (2) decreasing the total tasks execution time. Several simulation experiments are carried out to compare the proposed method with two well-known methods, called checkpointing with rollback recovery and hardware replication. Experimental results reveal that in the presence of multiple transient faults, the feasibility rate of the proposed method is considerably higher than the other well-known fault-tolerance methods. Moreover, the average timing overhead of this method is lower than the traditional methods.

Fault tolerant scheduling of hard real-time tasks on multiprocessor system using a hybrid genetic algorithm

Conventional methods for fault-tolerant scheduling of real-time tasks based on traditional heuristic approach offer poor performance and inefficient system utilization. The primary-backup (PB) approach is often used as a fault-tolerant scheduling technique to guarantee RT tasks to meet their deadline despite the presence of fault. We propose a novel scheduling algorithm using optimization approach based on genetic algorithm (GA) hybridized with knowledge from the real-time task scheduling domain for providing fault-tolerance (FT) in multiprocessor environment. Exhaustive simulation reveals that the new GA based primary-backup fault-tolerant scheduling (PBFTS) approach outperforms other fault-tolerant scheduling schemes in terms of system utilization and efficiency.

Fault-Tolerant Scheduling for Real-Time Tasks with QoS Requirements on Heterogeneous Clusters

PDF HTML阅读 XML下载 导出引用 引用提醒 异构集群系统中具有QoS 需求的实时任务容错调度 DOI: 10.3724/SP.J.1001.2011.03833 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家安全重大基础研究计划(973)(6136101); 国家高技术研究发展计划(863)(2008AA7070412) Fault-Tolerant Scheduling for Real-Time Tasks with QoS Requirements on Heterogeneous Clusters Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:容错调度是调度问题中一个重要的研究内容,是提高系统可靠性的有效手段.目前已有很多集群系统时任务的容错调度算法,但是这些算法都没有考虑到任务的QoS 需求问题.提出了一种异构集群系统中具有QoS 需求的实时任务容错调度算法FTQ(fault-tolerant QoS-based scheduling).该算法采用主版本/副版本(primary/backup, 简称PB)技术,综合考虑了任务的时间限制、任务的QoS 需求、系统的可靠性和系统资源的利用率,能够自适应地根据系统负载情况动态地调整任务的QoS 级别和副版本的执行模式,从而提高了系统的灵活性、可靠性、可调和资源的利用率.对系统的可靠性进行了定量分析,并将其引入到容错调度算法中,提高了系统的可靠性.同时,度过程中尽量提前主版本的开始时间,推迟副版本的开始时间,以使任务的副版本采用被动执行模式或者使任版本和副版本的重叠部分尽量少,提高了资源的利用率.此外,采用了副版本重叠技术,并分析了副版本的最晚时间及其约束条件,提高了任务的调度成功率.通过大量的模拟实验,对FTQ,NOFTQ 和DYFARS 算法进行了.实验结果表明,FTQ 算法的性能优于其他方法,具有更好的调度质量. Abstract:Fault-Tolerant scheduling, an effective means of improving a system’s performance, plays a significant role in scheduling research. Despite the fact that fault-tolerant scheduling has been extensively proposed for real-time tasks on clusters, QoS (quality of service) requirements for some tasks have not been considered. This paper proposes a fault-tolerance scheduling algorithm FTQ (fault-tolerant QoS-based scheduling) for real-time tasks with QoS needs on heterogeneous clusters. FTQ adopts the primary/backup model and takes the timing constraints of tasks, QoS requirements of tasks, reliability of systems, and system resource utilization into account. FTQ can adjust the QoS levels of real-time tasks and the execution schemes of backup copies to improve system flexibility, reliability, schedulability, and resource utilization. The system reliability is quantitatively measured and combined into FTQ, which improves the system performance. Meanwhile, FTQ strives to advance the start time of primary copies and delay the start time of backup copies to make backup copies adopt passive execution scheme, or decrease overlapping sections of primary and backup copies as much as possible to improve resource utilization. FTQ adaptively adjusts the QoS levels of tasks and the execution schemes of backup copies to attain a higher flexibility. The overlapping technology of backup copies is employed. The latest start time of backup copies and its constraints are analyzed. Compared with NOFTQ and DYFARS, FTQ shows obvious superiority with a higher scheduling quality proven by a considerable number of simulated experiments. 参考文献 相似文献 引证文献

Boosting adaptivity of fault-tolerant scheduling for real-time tasks with service requirements on clusters

Abstract Thank to the excellent extensibility and usability, computer clusters have become the dominating platform for parallel computing. Fault-tolerance is mandatory for safety-critical applications running on clusters. In this paper we propose a service-aware and adaptive fault-tolerant scheduling algorithm using overlapping technologies (SAO in short) that can tolerate a node’s permanent failure at any time instant for real-time tasks with service requirements in heterogeneous clusters. SAO adopts the primary/backup model and considers the timing constraints, service requirements, and system resource utilization. To improve system resource utilization, we employ backup- backup (BB in short) and primary- backup (PB in short) overlapping technologies and analyze the overlapping constraints. In addition, SAO has high system adaptivity by dynamically adjusting the service levels of tasks based on system load. Furthermore, to improve resource utilization and schedulability, SAO makes backup copies adopt passive execution scheme or decrease the overlapping execution time of the primary copy and backup copy of a task as much as possible. Compared with a baseline algorithm SAWO (a service-aware and adaptive fault-tolerant scheduling algorithm without using overlapping technologies) and an existing algorithm DYFARS with simulation experiments, SAO achieves an average of 51.25% improvement in performability.

Efficient overloading techniques for primary-backup scheduling in real-time systems

In real-time systems, tasks have deadlines to be met despite the presence of faults. Primary-Backup (PB) scheme is one of the most important schemes that has been employed for fault-tolerant scheduling of real-time tasks, wherein each task has two versions and the versions are scheduled on two different processors with time exclusion. There have been techniques proposed for improving schedulability of the PB-based scheduling, of which Backup–Backup (BB) overloading is among the most popular ones. In this technique two or more backups can share/overlap in time on a processor. In this paper, we propose two new techniques that accommodate more tasks and/or tolerate faults effectively. In the first technique, called dynamic grouping, the processors are dynamically grouped into logical groups in order to achieve efficient overloading of tasks on resources, thereby improving the schedulability and the reliability of the system. In the second technique, called PB overloading, the primary of a task can share/overlap in time with the backup of another task on a processor. The intuition is that, for a primary (or backup), the PB-overloading can assign an earlier start time than that of the BB-overloading, thereby increasing the schedulability. We conduct schedulability and reliability analysis of the proposed techniques through simulation and analytical studies. Our studies show that dynamic grouping improves the schedulability more than static grouping, and offers graceful degradation with increasing faults. Also, PB-overloading improves the schedulability more than BB-overloading, and offers reliability comparable to that of BB-overloading. The proposed techniques are generic that they can be incorporated into many fault-tolerant non-preemptive scheduling algorithms.

Efficient overloading techniques for primary-backup scheduling in real-time systems

In real-time systems, tasks have deadlines to be met despite the presence of faults. Primary-Backup (PB) scheme is one of the most important schemes that has been employed for fault-tolerant scheduling of real-time tasks, wherein each task has two versions and the versions are scheduled on two different processors with time exclusion. There have been techniques proposed for improving schedulability of the PB-based scheduling, of which Backup–Backup (BB) overloading is among the most popular ones. In this technique two or more backups can share/overlap in time on a processor. In this paper, we propose two new techniques that accommodate more tasks and/or tolerate faults effectively. In the first technique, called dynamic grouping, the processors are dynamically grouped into logical groups in order to achieve efficient overloading of tasks on resources, thereby improving the schedulability and the reliability of the system. In the second technique, called PB overloading, the primary of a task can share/overlap in time with the backup of another task on a processor. The intuition is that, for a primary (or backup), the PB-overloading can assign an earlier start time than that of the BB-overloading, thereby increasing the schedulability. We conduct schedulability and reliability analysis of the proposed techniques through simulation and analytical studies. Our studies show that dynamic grouping improves the schedulability more than static grouping, and offers graceful degradation with increasing faults. Also, PB-overloading improves the schedulability more than BB-overloading, and offers reliability comparable to that of BB-overloading. The proposed techniques are generic that they can be incorporated into many fault-tolerant non-preemptive scheduling algorithms.

A new scheduling approach supporting different fault-tolerant techniques for real-time multiprocessor systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met even in the presence of faults. Three different approaches have evolved for fault-tolerant scheduling of real-time tasks in multiprocessor systems — Triple Modular Redundancy (TMR), Primary Backup (PB), and Imprecise Computation (IC). In TMR approach, the fault detection is by voting, whereas in PB and IC approaches, it is by acceptance test. The different methods employed for error detection in the three approaches often make one approach preferable to the other in certain applications. Also, some applications can have tasks which require more than one fault-tolerant approach. Hence, it is necessary to have a single fault-tolerant scheduling algorithm which supports different fault-tolerant approaches. Moreover, the redundancy introduced in terms of executing more versions of a task reduces the number of tasks meeting their deadlines (guarantee ratio). In this paper, we address these two issues: (i) by proposing a scheduling algorithm which supports all three fault-tolerant approaches, and (ii) by proposing guarantee ratio improving techniques such as the distance concept and task parallelization, and better algorithms for reclaiming of unused resources at run-time.