In this article, we propose a general framework for fine-grain application-aware task management in heterogeneous embedded platforms, which allows integration of different mechanisms for an efficient resource utilization, frequency scaling, and task migration. The proposed framework incorporates several components for accurate runtime monitoring by relying on the OS facilities and performance self-reporting for parallel and iterative applications. The framework efficiency is experimentally evaluated on a real hardware platform, where significant power and energy savings are attained for SPEC CPU2006 and PARSEC benchmarks, by guiding frequency scaling and intercluster migrations according to the runtime application behavior and predefined performance targets.

Due to the dissemination of smartphones and tablets, a constant complexity growth can be observed for both embedded systems and mobile applications. However, this results in an increase in energy consumption. To guarantee longer battery life cycles, it is fundamental to develop system level strategies that allow guaranteeing the applications' required quality of service by managing the available system resources. In this paper a new task management framework is proposed that controls, in real-time, the execution of multi-threaded applications in order to meet their performance targets. For this, we amend the Linux CFS scheduler decisions to efficiently control the shared resource utilization of parallel applications. The proposed framework relies on runtime performance modelling of both the underlying architecture and the running applications to scale the system resource allocation and frequency. As a result, efficient application execution is achieved not only in terms of performance, but also in energy consumption. Experimental results show that the proposed approach satisfies the applications required performance level by decreasing the relative performance error from 2.801 to 0.168, while achieving 49 % energy savings.