In the multi-step traffic flow prediction task, the spatial-temporal feature extraction effect is not good and the prediction accuracy of future traffic flow is low. In order to solve these problems, a fusion model combining Long-Short Term Memory (LSTM) network, convolutional residual network and attention mechanism was proposed. Firstly, an encoder-decoder-based architecture was used to mine the temporal domain features of different scales by adding LSTM network into the encoder-decoder. Secondly, a convolutional residual network based on the Squeeze-and-Excitation (SE) block of attention mechanism was constructed and embedded into the LSTM network structure to mine the spatial domain features of traffic flow data. Finally, the implicit state information obtained from the encoder was input into the decoder to realize the prediction of high-precision multi-step traffic flow. The real traffic data was used for the experimental testing and analysis. The results show that, compared with the original graph convolution-based model, the proposed model achieves the decrease of 1.622 and 0.08 on the Root Mean Square Error (RMSE) for Beijing and New York traffic flow public datasets, respectively. The proposed model can predict the traffic flow efficiently and accurately.

A Web service Management System with Multiple Engines (WSMSME) was proposed to solve the problem of execution optimization for composite services in the system. The scheduler execution of composite services in system with multiple engines was analyzed, and a dynamic programming algorithm was put forward, which optimally minimized the heaviest load of engines by segmenting a pipelined execution plan into subsequences before they were dispatched and executed. Experiment with an initial prototype indicates that the algorithm can lead to significant performance improvement than the random algorithm.

The designing principle of large data driver buffer was studied. Ordinary data transfer strategies have their limitations either in time efficiency or performance stability. A multithreading positive waiting strategy based on dynamic feedback was proposed. Firstly, multithreading data transfer scheme was implemented in the form of main thread's positive wait. For high performance guarantees under unpredictable workloads condition, dynamic feedback was imported to balance the data and instruction transfer between server-to-driver and driver-to-client.