Design and performance analysis of distributed computing optimization algorithm for big data environment

Within the broad scope of big data governance, data analysis plays a crucial role. However, facing the challenges of processing large amounts of data, traditional data analysis methods often suffer from problems such as long processing times and limited computing resources, which seriously restrict the timeliness and practicality of data analysis. With the rapid advancement of information technology, especially the flourishing development of emerging technologies such as cloud computing, distributed computing, and data mining, new possibilities have been opened up for big data governance. Distributed systems (DFS), with their excellent parallel processing capabilities and high scalability, occupy a core position in the field of big data processing. In view of this, this article proposes a new DFS architecture scheme that integrates big data and particle swarm optimization algorithm (PSO). This scheme fully utilizes the advantages of PSO and combines the parallel processing capability of DFS, aiming to achieve rapid analysis and processing of massive datasets. The experimental results demonstrate that the system not only significantly improves the efficiency of data analysis and effectively reduces computational costs, but also significantly enhances the flexibility and reliability of data processing.