Distributed Database Load Balancing Prediction Based on Convolutional Neural Network

Abstract

Traditional database services have been unable to handle the data surge in terms of system scalability and price-performance ratio. Distributed database services are proposed to support the rapid development of enterprise services and are suitable for various applications in big data scenarios. Load balancing prediction method, an important part of distributed database services, is used to predict the current situation of distributed system resources occupancy. However, the traditional load balancing prediction algorithm has shortcomings in the accuracy of real-time prediction and dealing with sudden loading. This paper proposes a distributed database load balancing prediction method based on convolutional neural network, which further realizes better real-time load balancing prediction and effective adjustment of sudden loading. The simulation results show that the load balancing prediction method proposed in this paper can effectively utilize the performance of each node to predict the usage of distributed database resources and effectively adjust the sudden loading, which can avoid the waste of computing resources and ensure the computational efficiency.