In-memory database load balancing optimization for massive information processing of the Internet of Things

In order to improve the operation effect of the in-memory database for massive information processing of the Internet of Things, this paper combines the load balancing signal processing algorithm to carry out the load balancing optimization analysis of the in-memory database. According to the local transformation characteristics of non-stationary multi-component signals, an adaptive FSST algorithm is proposed in this paper. According to the signal separability condition, this paper uses the local Rayleigh entropy to estimate the window function parameters of the adaptive FSST and the adaptive FSST2. In addition, this paper adopts an adaptive window function to automatically match the local changes of the signal, so that the signal has the optimal energy aggregation in any part. The results show that when the number of concurrent users is the same, the time consumption, throughput and bandwidth of the proposed method are always higher than the method proposed in reference [10]. When the number of concurrent books is 97, the time of the proposed method is 45000ms, the time of the proposed method is 40000ms, the highest throughput of the proposed method is 2.30 MB/s, the highest bandwidth is 11.9MB/s, the highest throughput of the method proposed in reference [10] is 2.2 MB/s, and the highest bandwidth is 11.8MB/s. The load balancing optimization algorithm of the memory database for massive information processing of the Internet of Things has good results.