Aiming at the problem of increased response delay of microservice clusters caused by the exponential growth of the number of Internet users. A load balancing algorithm suitable for microservice clusters is proposed. By introducing dynamic weights to the least active number algorithm, when the load balancer receives a user request, the load balancer will select the server with the smallest active number to execute. If the active number of multiple servers is the same as the least active number at this time, use The CRITIC method recalculates the weights of memory size, memory usage, number of processor cores, processor usage, disk size, and disk usage indicators for servers with the same least active number, and combines each indicator to obtain the real-time performance quantification value of the server. Select the server with the best real-time performance quantification value for service. The experimental results show that, compared with the least active number algorithm before improvement, the proposed algorithm can reduce the overall response delay of the microservice cluster.
Aiming at the problem that the storage resources are challenging to use due to the fragmentation of storage resources of the Internet of Things terminal equipment group, this manuscript proposes a storage resource management strategy of minimum difference fragment storage. This manuscript builds a distributed storage architecture for terminal equipment groups among IoT terminal devices in the same local area network to realize file access and sharing among various terminal devices. When storing files, the minimum difference fragment storage method is adopted. Large files are stored in the device in a fragmented storage method; small files are stored in the device that the difference between the available storage resource size of the device and the file size is non-negative and the smallest. The simulation experiment results show that the strategy proposed in this manuscript can effectively improve the utilization of terminal equipment storage resources.
Aiming at the low accuracy of elderly speech emotion recognition, we propose an emotion recognition method that integrates the elderly's speech features and embeds the attention mechanism in this paper. The method firstly extracts the speech features of the elderly and fuse them. Then the fusion features are used as the bidirectional long and short-term memory network (BLSTM) input to learn the deep emotional features of each frame of speech. The attention mechanism uses to calculate the weight of the emotional classification of each frame feature. Finally, the features of each frame multiplied by their respective weight coefficient are used as the fully connected layer input to complete the recognition of speech emotion. The experimental results on the elderly speech emotion database (EESDB) show that compared with the traditional BLSTM, this method can effectively improve the accuracy of elderly speech emotion recognition.
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