Research on Architecture Optimization of Intelligent Cloud Platform and Performance Enhancement of MicroServices

Abstract

Intelligent cloud platforms have become a cornerstone in managing and operating complex business process systems. However, the microservice architectures underlying these platforms often face significant challenges in real-world applications, including tight coupling between services, intricate inter-service communication, inefficient resource scheduling, and difficulties in tracing and diagnosing service calls. To address these issues, this study proposes a comprehensive framework that integrates multiple key strategies: architectural decoupling to reduce interdependencies, advanced service governance for standardized and reliable service management, optimized resource scheduling to improve system efficiency, and end-to-end call chain diagnosis to enhance fault detection and performance monitoring. Based on these strategies, a high-performance intelligent cloud platform model is established, capable of achieving both operational efficiency and system reliability. The findings of this research not only provide a solid theoretical foundation but also offer practical guidelines for ensuring the stable operation, proactive maintenance, and intelligent management of microservice-based cloud platforms, ultimately supporting scalable and resilient enterprise IT infrastructures.