Self-optimizing database performance management system with reinforcement learning in AWS and Snowflake

Abstract

Self-tuning database performance management system (100) comprising: a) a monitoring module configured to collect real-time performance data from cloud-based databases hosted on AWS and Snowflake; b) a feature extraction module configured to convert performance data into a state representation; c) a reinforcement learning module configured to receive the state representation and generate voting actions; and d) an action execution module configured to apply the reconciliation actions to the database environment.

Description

The present invention relates to cloud database management systems, and more particularly to a self-tuning performance optimization system that uses reinforcement learning (RL) algorithms to autonomously manage and optimize performance parameters in cloud-based databases such as AWS and snowflake.In the Ara of digital transformation, the volume, velocity and variety of data have grown exponentially, leading to a widespread adoption of cloud-based database platforms such as Amazon Web Services (AWS) and Snowflake. These platforms provide scalable, resilient, and inexpensive solutions to the management and analysis of large data sets. However, ensuring optimal performance in these cloud environments remains a constant challenge. Database administrators (DBAs) and data technicians often need to adjust configurations manually, allocate computing resources, manage simultaneity, and optimize queries based on the developing workloads. This process is not only time consuming and prone to errors, but also inefficient in dynamic or large-scale environments where the usage patterns can change quickly and unpredictablely.Conventional rule-based or static optimization methods are often inadequate, since they cannot adapt to fluctuating workload requirements or new data access patterns in real time. In addition, different query types, user behavior, and resource constraints require different optimization strategies, so that it is nearly impossible to develop a manual optimization system that fits all of them. As companies seek to ensure low scan latency, high throughput, and cost efficiency, the need for smart automated solutions is becoming more and more stringent.Recent advances in machine learning, particularly reinforcement learning, are promising alternatives. Reinforcement learning models are able to learn from interaction with the environment, making them ideal for systems that require continuous self-optimization. In contrast to supervised learning, reinforcement learning does not rely on static training data, but learns optimal actions by probing and feedback in a real environment. When applied to cloud database management, reinforcement learning has the potential to revolutionize performance optimization by dynamically adjusting system parameters based on real-time metrics and reward feedback.Despite these possibilities, the integration of reinforcement learning into real database ecosystems such as AWS and snowflake entails some technical challenges. These include real-time telemetry data acquisition, action safe execution, performance impact assessment, and compliance with safety and compliance standards. Accordingly, there is a need for a robust, customizable system that can autonomously manage and tune the performance of cloud databases using reinforcement learning, thereby minimizing human intervention while maximizing efficiency, reliability, and cost savings. The present invention addresses these gaps by proposing a self-tuning power management system specifically designed for AWS and snowflake cloud environments.An object of the present disclosure is to enable autonomous database matching without manual interventions.Another object of the present disclosure is to continuously improve performance by reinforcement learning.Another object of the present disclosure is to reduce the scan latency and improve the overall responsiveness.Another object of the present disclosure is to optimize usage of computing resources to reduce operating costs.Another object of the present disclosure is to dynamically adapt to changes in workload in real-time.Another object of the present disclosure is seamless integration with AWS and snowflake cloud services.Another object of the present disclosure is operation within a secure and compliant cloud environment.Another object of the present disclosure is efficient scaling across different data workloads and user requests.The present invention relates generally to a fully automatic system that manages the performance of cloud-based databases on AWS and snowflake without human intervention. It accommodates workload changes and ensures consistent service levels through intelligent decision making.In one embodiment of the present invention, the system has a reinforcement learning module that continuously learns and refines its optimization strategies. It selects actions based on the observed conditions to maximize long term performance and cost efficiency.Another embodiment of the invention is the system having a monitoring module that captures real-time metrics such as scan latency, CPU usage, and I / O throughput. These metrics are used to make decisions and evaluate the effects of previous optimization actions.A further embodiment of the invention consists in carrying out a multiplicity of measures for power optimization, for example the automatic scaling of computing resources, the modification of SQL queries or the adaptation of caching rules. These actions are context-dependent and are directed to the current state of the database.Another embodiment of the invention is integration with AWS CloudWa, lambda functions and snowflake APIs to enable real-time interaction. It is designed to be cloud-native and platform-independent within AWS and snowflake environments.Another embodiment of the invention is that the system operates in a closed loop, in which the result of each action enters into future decisions. This circulation ensures that the model continues to develop and adapt to changing conditions.Another embodiment of the invention is that all operations within the system are performed with strict compliance with data security and government standards. API calls and data access patterns are encrypted, authenticated and checked.Another embodiment of the invention not only improves the query speed and responsiveness of the system, but also reduces unnecessary computational costs. This results in a powerful, cost-effective and scalable database infrastructure.The present invention relates to a self-optimizing database performance management system that uses reinforcement learning to optimize cloud database operation in AWS and snowflake. It includes key modules such as a real time data acquisition monitor module, a state representation feature extraction module, a decision making reinforcement learning module, and an action execution module for applying tuning changes. These modules operate in a closed loop feedback to continuously improve performance. The system dynamically adjusts computing resources, query structures, and configurations based on learned patterns. It operates autonomously, reducing manual effort and at the same time improving speed, efficiency and cost efficiency.The invention is explained again below with reference to the figure. The following shows: FIG. 1 is an illustration of the self-optimizing database performance management system (100) using reinforcement learning integrated in AWS and snowflake environmentsFIG. 1 illustrates the self-optimizing database performance management system (100) using reinforcement learning and incorporated in AWS and snowflake environments. The self-optimizing database performance management system continuously monitors and optimizes performance of cloud-based databases hosted on AWS and snowflake using reinforcement learning techniques. First, a monitoring module collects real-time telemetry data such as CPU usage, memory consumption, hard disk I / O, scan latency, inventory usage, and degrees of simultaneity from integrated sources such as AWS CloudWa and Snowflake views. This data is processed by a feature extraction module, which converts it into structured input representations suitable for a reinforcement learning (RL) model. The RL module, which can use algorithms such as deep Q networks (DQN) or proximal policy optimization (PPO), evaluates the state of the system and independently selects performance optimization actions that aim to optimize key metrics such as challenge throughput, latency, and cost efficiency. These actions, including operations such as virtual warehouse resize, auto-suspend threshold adjust, rewrite SQL query, recommend or apply materialized views, and change resource queues, are performed via secure API calls using AWS SDKs and the REST interfaces of snowflake. Once executed, the system monitors the effects of the actions on overall performance and cost. This feedback is fed back to the learning model to refine future decisions and thus enable continuous improvement. The system is designed to operate in a closed loop and dynamically adapt to workload and user behavior variations while maintaining the enterprise data management policies and cloud security protocols. Over time, the RL model will be more and more capable of selecting the most efficient optimization strategies for different operating scenarios, ultimately reducing the need for manual interventions and enabling consistent performance optimization in a scalable, cloud-native manner.

Claims

A self-tuning database performance management system (100) comprising: a) a monitoring module configured to collect real-time performance data from cloud-based databases hosted on AWS and snowflake; b) a feature extraction module configured to convert performance data to a state representation; c) a reinforcement learning module configured to receive the state representation and generate tuning actions; and d) an action execution module configured to apply the tuning actions to the database environment.The system (100) of claim 1, wherein the reinforcement learning module uses a deep Q network (DQN), proximal policy optimization (PPO), or advantage actor-critic (A2C) algorithm.The system (100) of claim 1, wherein the matching actions comprise at least one of: resize computing resources, modify SQL query structures, adjust caching rules, or change scheduling of data transformations.The system (100) of claim 1, wherein the monitoring module is integrated with AWS CloudWa and Snowflake Query History APIs.The system (100) of claim 1, wherein the action execution module communicates with cloud provider APIs to apply tuning changes.The system (100) of claim 1, further comprising a feedback loop configured to assess the effects of the tuning actions on important performance indicators and update the reinforcement learning model.

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