Research on Elastic Scaling and Deployment Inspection Metrics for Cloud Platform Business Systems in a Microservices Architecture

This study focuses on the exploration of elastic scalability online check index of power grid cloud platform business system under microservice architecture. The core of the research is the construction of the clustering mixing coefficient index, which combines the characteristic attributes of the grid cloud platform with the flexibility and efficiency requirements of the microservice architecture. People propose a preferred residual-weighted algorithm to optimize the accuracy of elastic predictions and ensure that system scaling strikes a balance between efficiency and real-time demand response. As an important part of verifying the model, the system simulation proves the validity of the model through fine algorithm design and strict data analysis. The simulation results emphasize that the system performance, especially the resource utilization efficiency and response time, can be significantly improved after the implementation of elastic expansion and scaling expansion strategies. This study contributes to the robustness and operational efficiency of the power grid cloud platform business system, and lays a solid foundation for the deployment of micro-service in a dynamically responsive cloud environment.