期刊文献+

一种基于资源预留的偶发任务状态反馈调度器

A Novel and Better Reservation-Based State Feedback Scheduler for Sporadic Tasks
在线阅读 下载PDF
导出
摘要 针对实时系统中的偶发任务和非周期任务,基于资源预留方法提出了一种利用状态反馈调整资源分配的调度策略。该策略的主要目标是当偶发作业负载在一定范围内变化时,保证偶发任务满足其时限,并使非周期任务的平均响应时间尽可能地小。文章首先通过状态方程描述偶发任务完成时间与预留资源的关系,然后利用状态反馈极点配置为偶发任务分配资源,最后将该闭环反馈调度系统看成是一种分段仿射系统,建立分段仿射二次型Lyapunov函数讨论算法的稳定性。最后通过实验说明,与传统调度算法相比,所提出的调度策略在调度负载不可预测的偶发任务时具有更好的性能。 Aim. The introduction of the full paper reviews some papers in the open literature, points out what we believe to be their shortcomings, and then proposes what we believe to be a novel and better scheduler. Sections 1 through 3 explain the design of our better scheduler. Section 1 briefs resource reservation scheduler. Section 2 briefs what is already known about resource allocation for different tasks. The core of section 3 consists of: ( 1 ) the goal of our scheduler is to make the sporadic jobs satisfy their deadlines when their loads vary in a certain range and to make the average response time of non-periodic jobs as short as possible; (2) after providing a precise mathematical model of the scheduler, a controller to adjust the resource allocation for different tasks is designed ; (3) the global stability of our scheduler is discussed as a piecewise affine system. Experimental results, presented in Figs. 2 through 6, and their analysis demonstrate preliminarily the effectiveness and advantage of our scheduling algorithm when the sporadic tasks vary unpredictably.
出处 《西北工业大学学报》 EI CAS CSCD 北大核心 2011年第5期719-724,共6页 Journal of Northwestern Polytechnical University
基金 国家自然科学基金青年科学基金(60803158/F0207)资助
关键词 偶发任务 资源预留 状态反馈调度 分段仿射系统 scheduling, algorithms, resource allocation, feedback control, evaluation, design, sporadic task, piecewise affine system
  • 相关文献

参考文献1

二级参考文献14

  • 1Maciejowski J M.Predictive control:with constraints.Prentice-Hall,2002.
  • 2Fletcher R.Practical methods of optimization 2nd ed.John Wiley & Sons Ltd,1991.
  • 3Zilberstein S.Operational rationality through compilation of anytime algorithms.Ph.D.thesis,University of California at Berkeley,1993.
  • 4Zilberstein S,Russell S J.Optimal composition of real-time systems.Artificial Intelligence,1996,82(1-2):181-213.
  • 5Hansen E A,Zilberstein S.Monitoring and control of anytime algorithms:a dynamic programming approach.Artificial Intelligence,2001,126(1-2):139-157.
  • 6Henriksson D,Cervin A,Akesson J,et al.On dynamic real-time scheduling of model predictive controllers.IEEE Conf.on Decision and Control,2002.
  • 7Henriksson D.Flexible implementation of model predictive controller using sub-optimal solutions.Technical Report,Lund Institute of Technology,2004.
  • 8Lu C,Stankovic J A,Son S H,et al.Feedback control real-time scheduling:framework,modeling and algorithms.Real-Time Systems,2002,23(1-2):85-126.
  • 9Cervin A,Eker J,Bemhardsson B.Feedback-feedforward scheduling of control tasks.Real-Time Systems,2002,23(1-2):25-53.
  • 10Abeni L,Buttazzo G.Integrating multimedia applications in hard real-time systems.In Proc.IEEE Real-Time Systems Symp.,1998.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部