Design And Implementation Of Key Value Database Load Balancing Technology Based On Intelligent Prediction

The key-value database is widely used in various distributed storage systems due to its excellent performance,and the load balancing of the database is conducive to improving the system stability and efficiency.In existing commercial databases,data access loads show periodic changes,and traditional load balancing strategies have scheduling lag in this scenario.With the development of deep learning,data prediction technology is becoming more and more mature.If the future load could be accurately predicted for a certain period of time,the hot data would be balanced in advance,which will effectively improve the performance of the database cluster.Based on this idea,this paper designs and implements a load balancing algorithm with the intelligent prediction.The algorithm is divided into two modules: the prediction module and the scheduling module.The prediction module trains the deep learning model by the historical data of the cluster to predict the future load curve of the cluster.Once the scheduling module is triggered to balance the hot data,the newly predicted load information of the cluster will be obtained to join the scheduling from the prediction module.With the given high-load threshold,the future low-load time will be calculated.A heat statistics algorithm is also designed with the heat attenuation characteristics to calculate the Top-K hot-data region.Given these information,a scheduling plan is generated.The scheduling plan is mainly executed during the low-load time interval to minimize the impact on performance,and the hot-data regions will be moved to balance the load in advance.The system prototype is implemented with the open source TiKV code and the LSTM model,and there are plenty of tests.The results show that the degree of fitting of the readworld load curve and the predicted load curve is between 93% and 97%;the average QPS peak value of the cluster using intelligent predictive load balancing scheduling is 1.24 times of the na(?)ve method,and the average query delay is reduced by 158 to 288 ms.