Our client is a leading Intelligent Automation Service Provider. They specialize in optimizing workflow automation and improving system performance through cutting-edge technology solutions.

The client needed to ensure high performance across web, microservices, and database layers. They faced scalability issues when handling increased concurrent users while maintaining system efficiency. Managing performance in a containerized cloud environment was another challenge. Identifying and resolving bottlenecks, such as database memory leaks, connection timeouts, and backend server connection issues, was a key concern. Additionally, implementing a structured approach to detect and resolve SLA breaches was essential for continuous monitoring and improvement.

We implemented a Performance Engineering Framework to evaluate and certify the performance of web, application, and database components in a QA environment. We conducted load testing using JMeter to establish performance baselines and rigorously assess system capabilities. AWS Cloud Performance Monitoring was utilized through AWS CloudWatch, enabling early issue detection and resolution. Scalability testing began with 50 concurrent users and was progressively scaled up to 400 users, identifying and resolving key performance bottlenecks along the way. Through optimization and continuous improvement, we detected SLA breaches and optimized configurations to enhance response times. System-level enhancements were also implemented, significantly improving scalability and reliability.

By leveraging reusable components, we saved over 200 hours per person across more than ten builds. More than 20 critical performance issues were identified and resolved, leading to significant improvements in system efficiency. The system's scalability improved fourfold, increasing capacity from 50 to 400 concurrent users. Optimized configurations resulted in improved response time and availability, ensuring a seamless user experience.