Citation:

Raghu Gopa, 2026. Autonomous Cloud Database Performance Optimization Using Reinforcement Learning in AWS and Snowflake  Volume 6 Issue 3: 01-06.

Abstract:

The emergence of cloud-based database systems has intensified the challenge of achieving optimal performance under large-scale workloads. Conventional tuning methods, which rely on human intervention and static heuristics, often struggle with the complexity and diversity of modern cloud environments. This survey offers an in-depth examination of the reinforcement learning (RL) techniques of automated optimisation of cloud database performance. By continuously observing workload behavior and system feedback, RL enables the database engines to become self-adaptive and reduces the need for continuous manual monitoring. The review describes the principles of RL and discusses its applicability to database tuning problems, including configuration-parameter optimisation, query-execution optimisation and resource-allocation optimisation. It also analyzes how the demand signal engineering can support sophisticated decision-making by incorporating real-time workload features and predictive analytics. It extends into the field of the RL-based optimisation implementation in AWS and Snowflake infrastructure highlighting benefits such as scalability, efficiency, and cost control. There are, however, several limitations of the method which include high training costs, potential inconsistency of exploratory stages and lack of interpretability. The areas of research to be pursued in the future are development of hybrid paradigms of learning, explainable AI as well as scalable optimisation frameworks that can be used to address these limitations. Combined, reinforcement learning is a timely paradigm of entirely autonomous, self optimising cloud database engines which dynamically adapt to changing workloads in real time in real time.

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Keywords:

Reinforcement Learning, Cloud Databases, Autonomous Optimization, AWS, Snowflake.