基于TCNＧA模型的高效查询负载预测算法

The query workload prediction algorithm based on a novel time series prediction model is proposed to address the problem of database management system cannot be optimized in time due to the dynamic change of query workload and the difficulty of forecasting effectively in the field of big data querying. First of all, the algorithm preprocesses the original historical users' queries by filtering, temporal interval partition and query workload construction to obtain the query workload sequence which is convenient for the network model to analyze and process. Secondly, the algorithm constructs a time series prediction model with temporal convolution network as the core, extracts the historical trend and auto-correlation characteristics of query workload-and realizes the time series prediction efficiently. At the same time, the algorithm integrates the designed temporal attention mechanism to weight the important query workloads to ensure that the query workload sequence can be analyzed and calculated efficiently by the model, and thus improving the performance of prediction algorithm. Finally, the algorithm uses the above time series prediction model to make full use of the query interval time to accurately predict the future query workloads-so that the database management system can achieve self-performance tuning in advance to adapt to the dynamic change of the workloads. Experimental results show that the designed query workload prediction algorithm exhibits good prediction performance on several evaluation metrics and is able to predict future query workload accurately over the query time interval.