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.