Abstract

In recent years, with the advent of the era of big data, the amount of data has shown explosive growth, and the demand for cloud computing resources is also increasing. How to optimize cloud computing resource allocation and improve resource utilization and system performance under big data environments has become an important research topic in the field of cloud computing. Based on the characteristics of big data processing, this paper proposes a cloud computing resource optimization strategy based on dynamic resource scheduling and data localization. By analyzing the architecture of the cloud computing platform, data localization, and task scheduling algorithms are introduced to optimize resource allocation and task execution efficiency. At the same time, this paper also designs a resource demand prediction model based on machine learning. This model achieves the prediction of future resource demand by analyzing and training historical data, thereby enhancing the resource utilization rate. Finally, it provides a new idea and method for the optimization of cloud computing resources by looking into the future development direction.