To improve the resource utilization with Quality of Service (QoS) guarantee, a task and resource scheduling method under Lyapunov optimization for container cloud queue was proposed. Firstly, based on the queueing model of cloud computing, the Lyapunov function was used to analyze the variety of the task queue length. Secondly, the minimum energy consumption objective function was constructed under the task QoS guarantee. Finally, Lyapunov optimization method was used to solve the minimum cost objective function to obtain an optimization scheduling policy for the online tasks and container resources, improving the resource utilization and guaranteeing the QoS. The CloudSim simulation results show that, the proposed task and resource scheduling policy achieves high resource utilization under the QoS guarantee, which realizes the online task and resource optimization scheduling of container cloud and provides necessary reference for cloud computing task and resource overall optimization based on queuing model.

Deep neural networks tend to suffer from overfitting problem because of the high complexity of the model. To reduce the adverse eeffects of the problem on the network performance, an improved elastic network model based deep learning optimization method was proposed. Firstly, considering the strong correlation between the variables, the adaptive weights were assigned to different variables of L1-norm in elastic network model, so that the linear combination of the L2-norm and the adaptively weighted L1-norm was obtained. Then, the solving process of neural network parameters under this new regularization term was given by combining improved elastic network model with the deep learning optimization model. Moreover, the robustness of this proposed model was theoretically demonstrated by showing the grouping selection ability and Oracle property of the improved elastic network model in the optimization of neural network. At last, in regression and classification experiments, the proposed model was compared with L1-norm, L2-norm and elastic network regularization term, and had the regression error decreased by 87.09, 88.54 and 47.02 and the classification accuracy improved by 3.98, 2.92 and 3.58 percentage points respectively. Thus, theory and experimental results prove that the improved elastic network model can effectively improve the generalization ability of deep neural network model and the performance of optimization algorithm, and solve the overfitting problem of deep learning.

The notion of k-error exhaustive entropy is proposed, which is based on exhaustive entropy used to measure the strength of random-like property of chaotic sequences, and its two basic properties are proved. Then the method is used to analyze the stability of random-like property of three common continuous chaotic systems, such as Lorenz system, R?ssler system, and Chua’s system. Simulation results show that the approach can reflect the random essence of continuous chaotic system and Chua’s system is better than Lorenz system and R?ssler system as the source of randomness.

Against the massive monitoring data of lightning locating system, a lightning nowcasting model based on Improved Density-Based Spatial Clustering of Application with Noise (IDBSCAN) clustering algorithm was put forward. Based on the lightning location data in real-time monitoring system, this method searched for lightning-density flash point greater than the threshold value of the land, built the cluster with up to the maximum ground flash density, and located the core of the cluster. Besides, with the application of adjacency list search algorithm, time and space consumed for the initial search set of lightning data had been greatly reduced. Furthermore, using regression fitting algorithm, the proposed algorithm can predict the path of movement of lightning cluster. The experimental results show that IDBSCAN algorithm used in the lightning nowcasting is effective.