High performance data processing of distributed database and multi-core processor based on particle swarm optimization

Abstract

: As a product of the combination of computer network technology and database technology, distributed database system has the characteristics of independence and transparency, centralized node combination, replication transparency and easy expansion. However, due to its complex access structure, distributed database system naturally has a high demand for query optimization. This paper proposes a high-performance data processing method between distributed database and multi-core processors based on PSO (Particle Swarm Optimization) to solve the task scheduling problem between multi-core processors. Inertia weight is introduced, which is added to the speed of particle flight to adjust the global and local search ability of stationary particles. The research results show that this method reduces the error rate of database query, and the overall performance of database query method is better. The improved PSO algorithm improves the searching ability of particles by dynamically adjusting the inertia weight. Therefore, the improved PSO is a high-performance algorithm to solve the real-time task scheduling problem of multi-core processors.