Integrating Artificial Intelligence to Automate Performance and Chaos Engineering in Cloud-Native Architectures

Abstract

Cloud-native architectures have revolutionized modern software systems, yet they present unique challenges in maintaining performance and reliability at scale. This article presents a comprehensive framework for integrating artificial intelligence into performance and chaos engineering processes, addressing the limitations of traditional manual testing approaches. This article introduces novel techniques for automating fault injection, performance optimization, and system resilience through machine learning and deep learning models. This article demonstrates how AI-driven automation can enhance the detection of performance bottlenecks, predict potential system failures, and facilitate real-time remediation in cloud-native environments. Through extensive case studies across e-commerce, financial services, and media streaming sectors, this article validates the effectiveness of its approach in improving system reliability and operational efficiency. The framework provides significant advantages over conventional methods by reducing human intervention, accelerating issue detection, and enabling proactive system optimization. Furthermore, it establishes a practical roadmap for organizations to implement AI-driven performance and chaos engineering, contributing to the evolving landscape of cloud-native architecture management. Future research directions and potential improvements are discussed, highlighting the transformative potential of AI automation in building resilient cloud-native systems.