Dynamic self-rescheduling of tasks over a heterogeneous platform
Modern applications require powerful highperformance platforms to deal with many different algorithms that make use of massive calculations. At the same time, low-cost and high-performance specific hardware (e.g., GPU, PPU) are rising and the CPUs turned to multiple cores, characterizing together an interesting and powerful heterogeneous execution platform. Therefore, self-adaptive computing is a potential paradigm for those scenarios as it can provide flexibility to explore the computational resources on heterogeneous cluster attached to a highperformance computer system platform. As the first step towards a run-time reschedule load-balancing framework targeting that kind of platform, application time requirements and its crosscutting behaviour play an important role for task allocation decisions. This paper presents a strategy for self-reallocation of specific tasks, including dynamic created ones, using aspect-oriented paradigms to address non-functional application timing constraints in the design phase. Additionally, as a case study, a special attention on Radar Image Processing will be given in the context of a surveillance system based on Unmanned Aerial Vehicles (UAV).