Cloud Computing Network Performance Optimization

The core factors determining the cloud computing network (CCN) performance are its structure and working strategy. This study investigated the internal working mechanism of key nodes and collaborative technology between nodes in CCN. Based on the analysis results, we implemented continuous address mapping for several key nodes on the access load processing path, including the load scheduling system, gateway system, application server system, and database system, solving the scalability within key nodes and efficient collaboration between nodes. Applying the read-write separation technology to the load scheduling system solved the problem of path separation between query load and write load in CCN. In the database system, the technologies of mass data segmentation, database publishing, and subscription are integrated, mitigating the problems of selection basis for the quantity of mass data segmentation and low efficiency of mass data processing. This improved the CNN structure and optimized its performance, avoiding several structural and policy design defects of conventional CCN. The performed numerical tests proved that the entire network effectively responded to user access requests even when a single key device failed. Moreover, the developed system exhibited improved performance in high concurrency access and massive data processing.