LEVERAGING AI TO IMPROVE PERFORMANCE TUNING IN POST-MIGRATION ORACLE CLOUD ENVIRONMENTS

Abstract

As enterprises increasingly migrate Oracle workloads to cloud platforms like Oracle Cloud Infrastructure (OCI) and Amazon Web Services (AWS), performance degradation post-migration has emerged as a critical concern. Despite the promised scalability and agility of cloud environments, variations in infrastructure, suboptimal configurations, and outdated performance tuning strategies can result in inefficient workloads and increased operational costs. Traditional methods such as manual AWR analysis, SQL plan management, and reactive diagnostics often fail to scale or adapt to the dynamic nature of cloud-native architectures. This paper explores how Artificial Intelligence (AI) is redefining performance tuning in post-migration Oracle environments. It highlights the role of AI-powered features—including autonomous indexing, predictive scaling, anomaly detection, and automated root cause analysis—in addressing tuning challenges across cloud-hosted Oracle databases. We analyze the integration of AI capabilities within Oracle’s ecosystem, such as the Autonomous Database and Oracle Management Cloud, as well as third-party AIOps tools. Through comparative analysis, architectural models, and practical guidance, we demonstrate how AI not only enhances the DBA's ability to maintain high-performance systems but also shifts their role from reactive firefighting to proactive performance engineering. The findings underscore that AI-driven performance tuning is not a replacement for database administrators, but rather an enabler of strategic optimization in complex, distributed cloud landscapes.