Engineering and Systems Integration for High-Performance Cloud-Native Microservices: A Performance Engineering Approach - Takkalapally, DevenderRao (International Journal of AI, BigData, Computational and Management Studies, 2026)
Cloud-native​‍​‌‍​‍‌ microservices have now been established as the primary method of creating scalable and resilient applications. However, the distributed nature of these services brings about several performance issues like increased latency, unpredictable throughput, and wasteful resource utilization, caused by dynamic orchestration and complex service interactions. There are many monitoring and tuning tools available to solve the problems, but the current cleaning methods are usually disjointed and reactive, dealing with performance issues only after they have worsened, and concentrating on single components rather than on the behavior of the entire system. This research highlights the significance of systems integration and performance engineering as a primary concern and puts forward a comprehensive, proactive performance engineering framework that integrates performance requirements with architecture design, service interfaces, observability, workload modeling, and continuous testing across the microservices lifecycle. The method is tested through a case study of a high-performance cloud-native microservices system installed in a container orchestration platform, thus leading to reductions of tail latency, increased throughput capabilities, ability to scale under peak load, and savings in costs in comparison with random tuning strategies. The article provides a well-organized framework and useful instructions demonstrating that it’s possible to significantly increase the reliability, scalability, and operational efficiency of cloud-native microservices in production through integrated, system-level performance ‍​‌‍​‍‌engineering.