Resources in cloud computing environment are dynamic and heterogeneous. The goal of resource allocation in large-scale tasks is to minimize the completion time and resource occupation while having the best load balancing, which is a Non-deterministic Polynomial (NP) problem. Drawing on the advantages of intelligent swarm optimization, a hybrid swarm intelligence scheduling strategy named Dynamic Random Distribution PSO (DRDPSO) was proposed based on an improved PSO algorithm. Firstly, the inertia weight constant of PSO was modified to be a variable to control the convergence speed of solution process reasonably. Secondly, the search scope of each iteration was shrinked so as to reduce invalid search on the premise of retaining candidate optimal set. Then, selection operation was introduced to select high-quality individuals and pass them on to the next generation. Finally, random disturbance was designed to improve the diversity of candidate solutions and avoid the local optimal trap to some extent. Two kinds of simulation tests were carried out on the CloudSim platform. The experimental results show that, the proposed DRDPSO is better than Simulated Annealing Genetic Algorithm (SAGA) and Genetic Algorithm (GA)+PSO in most cases when dealing with isomorphic tasks. The total execution time of the proposed algorithm is less than SAGA by 13.7%-37.0% and less than GA+PSO by 13.6%-31.6%, the resource consumption of the proposed algorithm is less than SAGA by 9.8%-17.1% and less than GA+PSO by 0.6%-31.1%, the number of iterations of the proposed algorithm is less than SAGA by 15.7%-60.2% and less than GA+PSO by 1.4%-54.7%, the load balance degree of the proposed algorithm is less than SAGA by 8.1%-18.5% and less than GA+PSO by 2.7%-15.3% with the smallest fluctuation amplitude. When dealing with heterogeneous tasks, three algorithms has the similar properties:in aspect of the total execution time consumption, CPU tasks are the most, the mixed tasks take the second place, and IO tasks are the least. The comprehensive performance of DRDPSO is the best, which is the most suitable for dealing with multiple types of heterogeneous tasks. GA+PSO algorithm is suitable for solving hybrid tasks and SAGA algorithm is suitable for solving IO tasks quickly. When dealing with large-scale isomorphic and heterogeneous tasks, the proposed DRDPSO can significantly shorten the total task execution time and improve the utilization of resources in varying degrees with proper load balancing of computing nodes.

A new method was proposed to accurately detect and quantitatively evaluate the lung nodule spiculation. First, the region growing method followed by level set method was used to accurately segment the main part of the lung nodule. Then, spiculated lines connected to the nodule boundary were extracted using a line detector in polar coordinates system. Finally, spiculation index was introduced as the quantitative measurement of spiculation features, which was then used as a criteria for distinguishing between spiculated and non-spiculated nodules. The consistency and correlation of spiculation index of the method and Lung Image Database Consortium (LIDC) were evaluated in detail. The experimental results show that the proposed method can effectively detect and quantitatively describe the lung nodule spiculation in CT images.