Research on Weighted Adaptive Particle Swarm Optimization Al-gorithm Based on Flink Stream Processing Framework

Apache Flink is one of the mainstream big data distributed computing engines. In distributed computing systems, task scheduling is crucial, and uneven load is a common scenario caused by heterogeneous clusters and uneven data distribution when processing data. To solve this problem, a solution based on weighted adaptive particle swarm optimization algorithm is proposed. In order to schedule tasks more accurately, the algorithm considers the priority of the task, the degree of data skew and other related factors, and weights them according to these factors to dynamically calculate the global optimal task allocation scheme. Experimental results show that compared with the default scheduling strategy of Flink, the proposed algorithm can effectively reduce the computational delay and improve the system performance.