Aiming at the specific application scenario of aircraft detection in large-scale satellite images of military airports, a real-time target detection and recognition framework was proposed. The deep Convolutional Neural Network (CNN) was applied to the target detection task and recognition task of aircraft in large-scale satellite images. Firstly, the task of aircraft detection was regarded as a regression problem of the spatially independent bounding-box, and a 24-layer convolutional neural network model was used to complete the bounding-box prediction. Then, an image classification network was used to complete the classification task of the target slices. The traditional target detection and recognition algorithm on large-scale images is usually difficult to make a breakthrough in time efficiency. The proposed target detection and recognition framework of aircraft based on CNN makes full use of the advantages of computing hardware greatly and shortens the executing time. The proposed framework was tested on a self-collected data set consistent with application scenarios. The average time of the proposed framework is 5.765 s for processing each input image, meanwhile, the precision is 79.2% at the operating point with the recall of 65.1%. The average time of the classification network is 0.972 s for each image and the Top-1 error rate is 13%. The proposed framework provides a new solution for application problem of aircraft detection in large-scale satellite images of military airports with relatively high efficiency and precision.

Considering the applications deployed in the cloud which are rather complicated and different applications exhibit different sensitivity to issues of specific resources, an architecture based main mode method was proposed to classify applications into CPU-intensive, memory-intensive, network-intensive, and I/O-intensive precisely, enabling better scheduling of resources in the cloud; An ARIMA (AutoRegressive Integrated Moving Average) model-based prediction algorithm, which was also implemented, can lower average prediction error (7.59% high average forecast error and 6.06% low average forecast error) when forecasting consumption of resources; Appropriate modifications have been made on the traditional virtualization-based application cloud architecture to solve the inflexibility and inefficiency of the architecture based on virtual machine.