Heterogeneous cluster-based multi/many-core platforms are on the edge, delivering high computing and energy-efficient embedded systems. These platforms support Dynamic Voltage/Frequency Scaling (DVFS), allowing to change the voltage/frequency levels for each cluster independently. Mapping dynamic applications on such platforms at run-time is a tedious task. This article presents a 0–1 Integer Linear Programming (ILP) based run-time management approach that aims to optimize the overall system energy. The proposed approach adopts a hierarchical management organization. A global management strategy determines application-to-cluster assignments and setups the cluster frequency configurations. A local management strategy determines task-to-core mapping in each cluster to minimize resource usage. Our approach achieves optimized solutions with reduced complexity and shows good scalability on different platform sizes. The experimental results show that, compared with the state-of-the-art approaches of similar complexity, the proposed global management strategy can reduce the average power consumption of the overall system by 80.3%. The experiment also demonstrates that resource minimization in the local management can significantly impact global management decisions, and thereby further reducing overall average power by up to 60.72%.

基于异构集群的多/多核平台处于边缘，可提供高性能计算和高能效嵌入式系统。这些平台支持动态电压/频率缩放（DVFS），允许独立更改每个群集的电压/频率水平。在运行时在此类平台上映射动态应用程序是一项繁琐的任务。本文介绍了一种基于0–1整数线性规划（ILP）的运行时管理方法，旨在优化整体系统能量。所提出的方法采用了分级管理组织。全局管理策略确定应用程序到集群的分配并设置集群频率配置。本地管理策略确定每个群集中的任务到核心的映射，以最大程度地减少资源使用。我们的方法以降低的复杂度实现了优化的解决方案，并在不同平台大小上显示了良好的可伸缩性。实验结果表明，与复杂程度类似的最新方法相比，所提出的全局管理策略可以将整个系统的平均功耗降低80.3％。该实验还表明，本地管理中的资源最小化会显着影响全局管理决策，从而进一步使总体平均功耗降低多达60.72％。