The application workloads in modern multicore platforms are becoming increasingly dynamic. It becomes challenging when multiple applications need to be executed in parallel in such systems. Mapping and scheduling of these applications are critical for system performance and energy consumption, especially in Network-on-Chip– (NoC) based multicore systems. These systems with multitasking processors offer a better opportunity for parallel application execution. Mapping solutions generated at design time may be inappropriate for dynamic workloads. To improve the utilization of the underlying multicore platform and cope with the dynamism of application workload, often task allocation is carried out dynamically. This article presents a hybrid task allocation and scheduling strategy that exploits the design-time results at runtime. By considering the multitasking capability of the processors, communication energy, and timing characteristics of the tasks, different allocation options are obtained at design time. During runtime, based on the availability of the platform resources and application requirements, the design-time allocations are adapted for mapping and scheduling of tasks, which result in improved runtime performance. Experimental results demonstrate that the proposed approach achieves an on average 11.5%, 22.3%, 28.6%, and 34.6% reduction in communication energy consumption as compared to CAM [18], DEAMS [4], TSMM [38], and CPNN [32], respectively, for NoC-based multicore platforms with multitasking processors. Also, the deadline satisfaction of the tasks of allocated applications improves on an average by 32.8% when compared with the state-of-the-art dynamic resource allocation approaches.

中文翻译：

---

具有多任务处理器的基于 NoC 的多核平台上的自适应任务分配和调度

现代多核平台中的应用程序工作负载变得越来越动态。当需要在此类系统中并行执行多个应用程序时，这变得具有挑战性。这些应用程序的映射和调度对于系统性能和能耗至关重要，尤其是在基于片上网络 (NoC) 的多核系统中。这些具有多任务处理器的系统为并行应用程序执行提供了更好的机会。在设计时生成的映射解决方案可能不适合动态工作负载。为了提高底层多核平台的利用率并应对动态的应用工作负载，任务分配往往是动态进行的。本文介绍了一种混合任务分配和调度策略，该策略在运行时利用设计时结果。通过考虑处理器的多任务处理能力、通信能量和任务的时序特性，在设计时获得不同的分配选项。在运行时，根据平台资源的可用性和应用程序需求，设计时分配适用于任务的映射和调度，从而提高运行时性能。实验结果表明，与 CAM [18]、DEAMS [4]、TSMM [38] 和 CPNN [32 ]，分别用于具有多任务处理器的基于 NoC 的多核平台。此外，分配的应用程序任务的截止日期满意度平均提高了 32。