The emergence of mobile and battery operated multimedia systems and the diversity of supported applications mount new challenges in terms of design efficiency of these systems which must provide a maximum application quality of service (QoS) in the presence of a dynamically varying environment. These optimization problems cannot be entirely solved at design time and some efficiency gains can be obtained at run-time by means of self-adaptivity. In this paper, we propose a new cross-layer hardware (HW)/software (SW) adaptation solution for embedded mobile systems. It supports application QoS under real-time and lifetime constraints via coordinated adaptation in the hardware, operating system (OS), and application layers. Our method relies on an original middleware solution used on both global and local managers. The global manager (GM) handles large, long-term variations whereas the local manager (LM) is used to guarantee real-time constraints. The GM acts in three layers whereas the LM acts in application and OS layers only. The main role of GM is to select the best configuration for each application to meet the constraints of the system and respect the preferences of the user. The proposed approach has been applied to a 3D graphics application and successfully implemented on an Altera FPGA. 1. Introduction The popularity of the embedded systems has been increasing over the last years and new products are rapidly and continually emerging [1]. These various systems share common characteristics as multimedia applications and internet-based services. The first main characteristic of such systems is a need for high performance capabilities for complex applications. The second characteristic is mobility. These systems must operate in varying conditions that depend on network bandwidth availability, on limited energy resources, and so forth. As such factors are known at run-time, these systems can benefit from self-adaptivity by using programmable or reconfigurable architectures [2]. Self-adaptivity means that the system can modify dynamically the way services are provided according to imposed constraints using automata theory, for instance. Thus, the system has several states and each of them corresponds to a set of working parameters: the number of executed applications, the HW resources used, and the allocated real-time operation system (RTOS) services [3]. Various adaptation techniques have been proposed in respect of the constraints of the system while giving a better quality of service. These techniques can act at any of the three different levels:
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