Study on Dynamic Principal Component Allocation Strategy for Data in Cloud-Fog Hybrid Management System

In a cloud-fog hybrid environment, the introduction of multi-agent architecture makes the dynamic resource allocation and rapid task offloading decision-making process more efficient, which are critical for optimizing system performance. The combination of cloud computing and edge computing integrates into a heterogeneous network that combines both. This network architecture leverages the complementary strengths of cloud and edge computing, maximizing their advantages while minimizing their weaknesses. The cloud utilizes its powerful computing and storage capabilities to provide flexible configurations, low latency, and high bandwidth network environments for edge devices. This paper employs principal component analysis to address the large amount of data generated in cloud-fog hybrid management systems and conducts dynamic analysis for resource allocation strategies. The principal component analysis algorithm extracts both dynamic and static features of the data, revealing hidden patterns within it. After analysis, this enhances understanding of the data and constructs a feature-based principal component model that adapts to changes in the cloud-fog environment. This model can also adjust the allocation plan of the cloud-fog hybrid system in real-time based on current system resource status, leading to more efficient resource utilization. Compared with the traditional data allocation strategy, the strategy proposed in this study has significant advantages in improving the system response speed, saving network bandwidth, and enhancing the organization efficiency of the related devices, which can balance the computational power demand of IoT data processing more efficiently.