Real-time task reconfiguration support applied to an UAV-based surveillance system

Modern surveillance systems, such as those based on the use of unmanned aerial vehicles, required powerful high-performance 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, reconfigurable computing is a potential paradigm for those scenarios as it can provide flexibility to explore the computational resources on heterogeneous cluster attached to a high-performance computer system platform. As the first step towards a run-time reconfigurable workload balancing framework targeting that kind of platform, application time requirements and its crosscutting behavior play an important role for task allocation decisions. This paper presents a strategy to reallocate specific tasks in a surveillance system composed by a fleet of unmanned aerial vehicles using aspect-oriented paradigms in order to address non-functional application timing constraints in the design phase. An aspect support from a framework called DERAF is used to support reconfiguration requirements and provide the resource information needed by the reconfigurable load-balancing strategy. Finally, for the case study, a special attention on radar image processing will be given.