The traditional lightning data forecasting methods often use single optimal machine learning algorithm to forecast, not considering the spatial and temporal variations of meteorological data. For this phenomenon, an ensemble learning based multi-machine learning model was put forward. Firstly, attribute reduction was conducted for meteorological data to reduce dimension; secondly, multiple heterogeneous machine learning classifiers were trained on data set and optimal base classifier was screened based on predictive quality; finally, the final classifier was generated after weighted training for optimal base classifier by using ensemble strategy. The experimental results show that, compared with the traditional single optimal algorithm, the prediction accuracy of the proposed model is increased by 9.5% on average.

The Dynamic Time Warping (DTW) algorithm cannot keep high classification accuracy while improving the computation speed. In order to solve the problem, a Coarse-Granularity based Dynamic Time Warping (CG-DTW) algorithm based on the idea of naive granular computing was proposed. First of all, the better temporal granularities were obtained by computing temporal variance features, and the original series were replaced by granularity features. Then, the relatively optimal corresponding temporal granularity was obtained by executing DTW with dynamically adjusting intergranular elasticity of granularities compared. Finally, the DTW distance was calculated in the case of the corresponding optimal granularity. During this progress, an early termination strategy of lower bound function was introduced for further improving the CG-DTW algorithm efficiency. The experimental results show that, the proposed algorithm was better than classical algorithm in running rate with increasing by about 21.4%, and better than dimension reduction strategy algorithm in accuracy with increasing by about 32.3 percentage points.Especially for the long time sequences classification, CG-DTW takes consideration into both high computing speed and better classification accuracy. In actual applications, CG-DTW can adapt to long time sequences classification with uncertain length.

In cloud resource sharing service model, in order to realize the multi-attribute range query of cloud resources, an improved E-SkipNet network was proposed. Firstly, based on the traditional Distributed Hash Table (DHT) network SkipNet, data attributes were added to the setting of NameID and physical nodes were added to single attribute domain to support multi-attribute range queries in E-SkipNet. Secondly, on the basis of the original E-SkipNet network, the physical nodes were simultaneously mapped into multiple logical nodes and added to multiple attribute domains, and the resources were released in accordance with different attributes to different logical nodes in the improved E-SkipNet. Finally, the resources were mapped to logical nodes utilizing uniform locality preserving hashing function, which was the key to support efficient range query. The simulation results show that the routing efficiency of improved E-SkipNet network was respectively increased by 18.09% and 20.47% compared with E-SkipNet and Multi-Attribute Addressable Network (MAAN). The results show that the improved E-SkipNet can support more efficient cloud resource multi-attribute range queries and achieve load balancing in heterogeneous environment.