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Enhanced Utility Accrual Scheduling Algorithms for Adaptive Real Time System | Science Publications

机译:自适应实时系统的增强型效用应计调度算法科学出版物

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> Problem statement: This study proposed two utility accrual real time scheduling algorithms named as Preemptive Utility Accrual Scheduling (PUAS) and Non-preemptive Utility Accrual Scheduling (NUAS) algorithms. These algorithms addressed the unnecessary abortion problem that was identified in the existing algorithm known as General Utility Scheduling (GUS). It is observed that GUS is inefficient for independent task model because it simply aborts any task that currently executing a resource with lower utility when a new task with higher utility requests the resource. The scheduling optimality criteria are based on maximizing accrued utility accumulated from execution of all tasks in the system. These criteria are named as Utility Accrual (UA). The UA scheduling algorithms are design for adaptive real time system environment where deadline misses are tolerable and do not have great consequences to the system. Approach: We eliminated the scheduling decision to abort a task in GUS and proposed to preempt a task instead of being aborted if the task is preemptive able. We compared the performances of these algorithms by using discrete event simulation. Results: The proposed PUAS algorithm achieved the highest accrued utility for the entire load range. This is followed by the NUAS and GUS algorithms. Conclusion: Simulation results revealed that the proposed algorithms were more efficient than the existing algorithm, producing with higher accrued utility ratio and less abortion ratio making it more suitable and efficient for real time application domain.
机译: > 问题陈述:本研究提出了两种效用应计实时调度算法,即抢先效用应计调度(PUAS)和非抢先效用应计调度(NUAS)算法。这些算法解决了不必要的堕胎问题,该问题已在称为通用效用调度(GUS)的现有算法中确定。可以看出,GUS对于独立任务模型效率不高,因为当具有较高效用的新任务请求资源时,它会简单地中止当前正在执行具有较低效用的资源的任何任务。调度最优性标准基于最大化从系统中所有任务的执行累积的应计效用。这些标准被称为效用累计(UA)。 UA调度算法是为适应性实时系统环境而设计的,在该环境中,期限丢失是可以容忍的,并且不会对系统造成重大影响。 方法:我们取消了在GUS中中止任务的计划决策,并建议先占任务而不是先占任务,然后再中止任务。我们通过使用离散事件仿真比较了这些算法的性能。 结果:所提出的PUAS算法在整个负载范围内都获得了最高的应计效用。其次是NUAS和GUS算法。 结论:仿真结果表明,与现有算法相比,所提算法具有更高的效率,产生的效用率更高,流产率更低,因此更适合实时应用领域。

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