Modern battery powered Embedded Systems (ES) must provide a high performance with minimal energy consumption to enhance the user experience. However, these two are often conflicting objectives. In current ES resource management techniques, user behavior and preferences are only indirectly or not at all considered. In this paper, we present a novel user- and battery-aware resource management framework for multi-processor architectures that considers these conflicting requirements and dynamic unknown workloads at run-time to maximize user satisfaction. Proposed technique learns user's habits to dynamically adjust the resource management schemes based on the data it collects regarding user's plug-in behavior, battery charge status, and workloads variability at run-time. This information is used to improve the balance between performance and energy consumption, and thus optimize the Quality of Experience (QoE). Our evaluation results show that our framework enhances the user experience by 22% in comparison with the existing state-of-the-art.     «

Modern battery powered Embedded Systems (ES) must provide a high performance with minimal energy consumption to enhance the user experience. However, these two are often conflicting objectives. In current ES resource management techniques, user behavior and preferences are only indirectly or not at all considered. In this paper, we present a novel user- and battery-aware resource management framework for multi-processor architectures that considers these conflicting requirements and dynamic unkn...     »

embedded systems; multiprocessing systems; power aware computing; resource allocation; Embedded multicore processors; modern battery; embedded multicore processors; user-centric resource management; battery charge status; plug-in behavior; resource management schemes; user satisfaction; dynamic unknown workloads; conflicting requirements; multiprocessor architectures; battery-aware resource management framework; user behavior; management techniques; ES; user experience; minimal energy consumption; Resource Management; Personalization; Quality of Experience; User-awareness; Battery-awareness; Performance; Energy Consumption; Heterogeneous; Multi-processor     «

embedded systems; multiprocessing systems; power aware computing; resource allocation; Embedded multicore processors; modern battery; embedded multicore processors; user-centric resource management; battery charge status; plug-in behavior; resource management schemes; user satisfaction; dynamic unknown workloads; conflicting requirements; multiprocessor architectures; battery-aware resource management framework; user behavior; management techniques; ES; user experience; minimal energy consumptio...     »