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Multiprocessor Real-Time Systems with Shared Resources: Utilization Bound and Mapping

机译:具有共享资源的多处理器实时系统:利用率限制和映射

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In real-time systems, both scheduling theory and resource access protocols have been studied extensively. However, there is very limited research on scheduling algorithms for real-time systems with shared resources, where the problem becomes more prominent with the emergence of multicore processors. In this paper, focusing on partitioned-EDF scheduling and MSRP resource access protocol, we study the utilization bound and efficient task mapping schemes for a set of periodic real-time tasks that access shared resources in multiprocessor/multicore systems. Specifically, with synchronization overhead being considered, we illustrate the schedulability anomaly for such systems. We develop the first synchronization-cognizant utilization bound and further analyze its non-monotonicity where the bound can decrease when more processors are deployed. Then, we show that finding the optimal mapping for tasks with shared resources is NP-hard. Based on a novel approach that iteratively tightens the synchronization overhead, we propose two efficient synchronization-cognizant task mapping algorithms (SC-TMA) with the goal of achieving better schedulability and balanced workload on deployed processors. Finally, the proposed SC-TMA schemes are evaluated through extensive simulations with synthetic tasks. The results show that, the schedulability ratio and (average) system load under SC-TMA are close to that of an INLP (Integer Non-Linear Programming) based solution for small task systems. When compared to the existing task mapping algorithms, SC-TMA obtain much better schedulability ratio and lower/balanced workload on all processors.
机译:在实时系统中,调度理论和资源访问协议都得到了广泛的研究。但是,对于具有共享资源的实时系统的调度算法的研究非常有限,随着多核处理器的出现,问题变得更加突出。在本文中,我们着重于分区EDF调度和MSRP资源访问协议,研究了在多处理器/多核系统中访问共享资源的一组周期性实时任务的利用率约束和高效任务映射方案。具体来说,在考虑了同步开销的情况下,我们说明了此类系统的可调度性异常。我们开发了第一个同步认知利用率限制,并进一步分析了其非单调性,当部署更多处理器时,该限制会降低。然后,我们表明,找到具有共享资源的任务的最佳映射是NP难的。基于一种迭代地加紧同步开销的新颖方法,我们提出了两种有效的同步识别任务映射算法(SC-TMA),目的是在部署的处理器上实现更好的可调度性和平衡的工作负载。最后,通过具有综合任务的大量仿真对提出的SC-TMA方案进行了评估。结果表明,SC-TMA下的可调度性比率和(平均)系统负载接近于基于INLP(整数非线性编程)的小型任务系统解决方案。与现有的任务映射算法相比,SC-TMA在所有处理器上都具有更好的可调度性比率和较低/平衡的工作量。

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