Energy-Performance Co-Management of Mixed-Sensitivity Workloads on Heterogeneous Multi-core Systems

Abstract

Satisfying performance of complex workload scenarios with respect to energy consumption on Heterogeneous Multi-core Platforms (HMPs) is challenging when considering i) the increasing variety of applications, and ii) the large space of resource management configurations. Existing run-time resource management approaches use online and offline learning to handle such complexity. However, they focus on one type of application, neglecting concurrent execution of mixed sensitivity workloads. In this work, we propose an energy-performance co-management method which prioritizes mixed type of applications at run-time, and searches in the configuration space to find the optimal configuration for each application which satisfies the performance requirements while saving energy. We evaluate our approach on a real Odroid XU3 platform over mixed-sensitivity embedded workloads. Experimental results show our approach provides 54% lower performance violation with 50% higher energy saving compared to the existing approaches.