Concurrent Application Bias Scheduling for Energy Efficiency of Heterogeneous Multi-Core platforms

Minimizing energy consumption of concurrent applications on heterogeneous multi-core platforms is challenging given the diversity in energy-performance profiles of both the applications and hardware. Adaptive learning techniques made the exhaustive Pareto-optimal space exploration practically feasible to identify an energy-efficient configuration. The existing approaches consider a single application's characteristic for optimizing energy consumption. However, an optimal configuration for a given single application may not be optimal when a new application arrives. Although some related works do consider concurrent applications scenarios, these approaches overlook the weight of total energy consumption per application, restricting those from prioritizing among applications. We address this limitation by considering the mutual effect of concurrent applications on system-wide energy consumption to adapt resource configuration at run-time. We characterize each application's power-performance profile as a weighted bias through off-line profiling. We infer this model combined with an on-line predictive strategy to make resource allocation decisions for minimizing energy consumption while honoring performance requirements. The proposed strategy is implemented as a user-space process and evaluated on a heterogeneous hardware platform of Odroid XU3 over the Rodinia benchmark suite. Experimental results show up to 61% of energy saving compared to the standard baseline of Linux governors and up to 27% of energy gain compared to state-of-the-art adaptive learning-based resource management techniques.