From Autonomic Computing to Self-Driving Databases: AI-Driven Autonomous Operations in Cloud Environments
DOI:
https://doi.org/10.15662/IJRAI.2021.0401004Keywords:
Autonomous Databases, Autonomic Computing, Cloud Infrastructure, Artificial Intelligence, Self-Managing Systems, Database Operations, High Availability, Self-Driving DatabasesAbstract
The increasing scale, complexity, and heterogeneity of modern data platforms have decisively outpaced the capabilities of traditional, manually operated database administration models, which were originally designed for relatively static, centralized environments. As enterprises migrate mission-critical workloads to cloud and hybrid architectures, database operations must now contend with elastic infrastructure, geographically distributed deployments, multi-tenant resource contention, and continuously evolving workload patterns. These shifts significantly amplify the operational burden associated with performance tuning, high-availability management, security patching, compliance enforcement, and capacity planning, often exceeding the practical limits of human-driven processes. In response, autonomous database operations systems endowed with self-monitoring, self-analysis, self-optimization, and self-healing capabilities have emerged as a compelling paradigm for sustaining reliability and performance at scale. This article traces the evolution of autonomous database systems through the foundational principles of autonomic computing, early self-managing database research, and the maturation of cloud-native infrastructure platforms. By synthesizing theoretical models such as the MAPE-K control loop with real-world implementations from IBM and Oracle, we examine how AI and machine learning techniques enable continuous, closed-loop operational decision-making across monitoring, diagnosis, planning, and execution phases. We further analyze the architectural trade-offs, organizational impacts, and trust considerations inherent in delegating operational control to intelligent systems, and outline future research directions focused on explainability, governance, and resilience in AI-driven autonomous database platforms.
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