How to distribute and coordinate tasks in cloud computing is a challenging issue, in order to get optimal resource utilization and avoid overload. In this paper, we present a novel approach on load balancing via ant colony optimization (ACO), for balancing the workload in a cloud computing platform dynamically. Two strategies, forward-backward ant mechanism and max-min rules, are introduced to quickly find out the candidate nodes for load balancing. We formulate pheromone initialization and pheromone update according to physical resources under the cloud computing environment, including pheromone evaporation, incentive, and punishment rules, etc. Combined with task execution prediction, we define the moving probability of ants in two ways, that is, whether the forward ant meets the backward ant, or not, in the neighbor node, with the aim of accelerating searching processes. Simulations illustrate that the proposed strategy can not only provide dynamic load balancing for cloud computing with less searching time, but can also get high network performance under medium and heavily loaded contexts.

This paper firstly gives a detail analysis on the security problems faced in cognitive radio network, and introduces the basic issues about cognitive radio network. Then, according to the differences between cognitive radio network and existing wireless network, it analyses and discusses the dynamic spectrum access security and artificial intelligence. Finally, it draws a conclusion to security problems of cross layer design

Image compression is a crucial step in image processing area. Image Fourier transforms is the classical algorithm which can convert image from spatial domain to frequency domain. Because of its good concentrative property with transform energy, Fourier transform has been widely applied in image coding, image segmentation, image reconstruction. This paper adopts Radix-4 Fast Fourier transform (Radix-4 FFT) to realize the limit distortion for image coding, and to discuss the feasibility and the advantage of Fourier transform for image compression. It aims to deal with the existing complex and time-consuming of Fourier transform, according to the symmetric conjugate of the image by Fourier transform to reduce data storage and computing complexity. Using Radix-4 FFT can also reduce algorithm time-consuming, it designs three different compression requirements of non-uniform quantification tables for different demands of image quality and compression ratio. Take the standard image Lena as experimental data using the presented method, the results show that the implementation by Radix-4 FFT is simple, the effect is ideal and lower time-consuming

In recent years, the text data of text mining has gradually become a new research topic. Among them, the study of the text clustering has attracted wide attention. This paper proposes an improved fuzzy clustering-text clustering method based on the fuzzy C-means clustering algorithm and the edit distance algorithm. We use the feature evaluation to reduce the dimensionality of high-dimensional text vector. Because the clustering results of the traditional fuzzy C-means clustering algorithm lack the stability, we introduce the high-power sample point set, the field radius and weight. Due to the boundary value attribution of the traditional fuzzy C-means clustering algorithm, we recommend the edit distance algorithm. The results show that the improved algorithm is applied to the text clustering, making the results of clustering more stable and accurate than the traditional FCM clustering algorithm.