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Resource Augmentation Bounds for Approximate Demand Bound Functions

机译:近似需求界函数的资源扩充界

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In recent work, approximation of the demand bound function for a sporadic task uses a linear approximation when the interval length of interest is larger than the relative deadline of the task. Such an approximation leads to a factor 2 for resource augmentation under a naive analysis, i.e., if the schedulability test using this approximate demand bound function fails, the task set is not schedulable by slowing down the system to 50% of the original speed. In this paper we provide a tighter analysis of such an approach on uniprocessor systems and on identical multiprocessor systems with partitioned scheduling under the earliest-deadline-first strategy. For uniprocessor systems, we prove that the resource augmentation factor is at most (2e-1)/e, where e is the Euler number. For identical multiprocessor systems with M processors, with respect to resource augmentation, we show that deadline-monotonic partitioning with approximate demand bound functions leads to a factor (3e-1)/e-1/M for constrained-deadline task sets and a factor 3-1/M for arbitrary-deadline task sets, in which the best results known so far are 3-1/M for constrained-deadline ones and 4-2/M for arbitrary-deadline ones. Moreover, we also provide concrete input instances to show that the lower bound of resource augmentation factors for uniprocessor systems (identical multiprocessor systems under an arbitrary order of fitting and a large number of processors, respectively) under such approaches is 1.5 (2.5, respectively).
机译:在最近的工作中,当感兴趣的间隔长度大于任务的相对期限时,零星任务的需求约束函数的近似使用线性近似。这样的近似导致在幼稚分析下资源增加的因数2,即,如果使用该近似需求约束函数的可调度性测试失败,则无法通过将系统速度降低到原始速度的50%来调度任务集。在本文中,我们提供了对这种方法的更严格的分析,该方法在最早的,先到先得的策略下,在具有分区调度的单处理器系统和相同的多处理器系统上进行。对于单处理器系统,我们证明资源增加因子最大为(2e-1)/ e,其中e是欧拉数。对于具有M个处理器的相同多处理器系统,在资源增加方面,我们表明具有受约束的近似需求约束函数的截止单调分区导致约束截止任务集的因子(3e-1)/ e-1 / M和一个因子任意期限任务集为3-1 / M,其中迄今为止已知的最佳结果是,强制期限任务集为3-1 / M,而任意期限任务集为4-2 / M。此外,我们还提供了具体的输入实例,以表明在这种方法下,单处理器系统(分别处于任意拟合顺序和大量处理器的相同多处理器系统)的资源增加因子的下限为1.5(分别为2.5) 。

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