Wind energy is a renewable energy source with great development potential, and a reliable and accurate prediction of wind speed is the basis for the effective utilization of wind energy. Aiming at hyperparameter optimization in a combined forecasting method, a wind speed prediction model based on the long short‐term memory (LSTM) neural network optimized by the firework algorithm (FWA) is proposed. Focusing on the real‐time sudden change and dependence of wind speed data, a wind speed prediction model based on LSTM is established, and FWA is used to optimize the hyperparameters of the model so that the model can set parameters adaptively. Then, the optimized model is compared with the wind speed prediction based on other deep neural architectures and regression models in experiments, and the results show that the wind speed model based on FWA‐improved LSTM reduces the prediction error when compared with other wind speed prediction‐based regression methods and obtains higher prediction accuracy than other deep neural architectures.

As an important part of data management, network traffic evaluation and prediction can not only find network anomalies but also judge the future trends of the network. To predict network traffic more accurately, a novel hybrid model, integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) with long short-term memory neural network (LSTM) optimized by the improved particle swarm optimization (IPSO) algorithm, is established for network traffic prediction. Firstly, an LSTM prediction model for the real-time mutation and dependence of network traffic is constructed, and the IPSO is applied to optimize the hyperparameters. Then, CEEMDAN is introduced to decompose sequences of raw network traffic data into several different modal components containing different information to reduce the complexity of the network traffic sequence. Finally, the evaluation of the experiments shows the feasibility and effectiveness of the proposed method by comparing it with other deep neural architectures and regression models. The results show that the proposed model CEEMDAN-IPSO-LSTM produced a significantly superior performance with a reduction of the prediction error.

The amount of encoded data replication in an erasure-coded clustered storage system has a great impact on the bandwidth consumption and network latency, mostly during data reconstruction. Aimed at the reasons that lead to the excess data transmission between racks, a rack aware data block placement method is proposed. In order to ensure rack-level fault tolerance and reduce the frequency and amount of the cross-rack data transmission during data reconstruction, the method deploys partial data block concentration to store the data blocks of a file in fewer racks. Theoretical analysis and simulation results show that our proposed strategy greatly reduces the frequency and data volume of the cross-rack transmission during data reconstruction. At the same time, it has better performance than the typical random distribution method in terms of network usage and data reconstruction efficiency.

The dramatic increase of storage devices in distributed storage cluster system and the inherent characteristics of distributed deployment mode make network resources become one of the bottlenecks of data storage process. By analyzing the functional characteristics of data flow transmission of the components of the storage system, the network topology structure of the storage system is constructed, and the evaluation index of node flow load is put forward based on the degree centrality and the betweenness centrality theory of the network to explore the network topology and real-time flow characteristics. According to the evaluation index of node flow load, a network-aware data layout scheme is proposed. By balancing the flow load of bottleneck link, congestion and transmission delay can be reduced to further shorten the total task execution time and improve the efficiency of data writing.