High-performance platforms are required by modern applications that make use of massive calculations. Actually, low-cost and high-performance specific hardware (e.g. GPU) can be a good alternative along with CPUs, which turned to multiple cores, forming powerful heterogeneous desktop execution platforms. Therefore, self-adaptive computing is a promising paradigm as it can provide flexibility to explore different computing resources, on which heterogeneous cluster can be created to improve performance on different execution scenarios. One approach is to explore run-time tasks migration among node's hardware towards an optimal system load-balancing aiming at performance gains. This way, time requirements and its crosscutting behavior play an important role for task (re)allocation decisions. This paper presents a self-rescheduling task strategy that makes use of aspect-oriented paradigms to address non-functional application timing constraints from earlier design phases. A case study exploring Radar Image Processing tasks is presented to demonstrate the proposed approach. Simulations results for this case study are provided in the context of a surveillance system based on Unmanned Aerial Vehicles (UAVs).