Cognitive Graph Database Parameter Tuning via Reinforcement Learning
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Solution Overview
Problem
Tuning a graph database for optimal performance requires significant time and expertise, and settings optimized for one hardware configuration are often difficult to reuse on different configurations, leading to costly and error-prone efforts.
Innovation Solution
A system using cognitive computing techniques, including deep reinforcement learning and Monte Carlo tree search, automatically generates optimized parameter settings for graph databases, optimizing data import and querying performance across varying hardware configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual tuning by human experts is used to optimize graph database performance, then performance can be improved, but significant time and expertise are required
Solution Approach 1:
The system enables self-service automation where the graph database system automatically performs parameter tuning without human expert intervention. The automated tuning system evaluates performance metrics and adjusts parameters independently, eliminating the need for manual expert tuning while maintaining optimized performance.
Solution Approach 2:
The patent replaces the mechanical process of manual expert tuning with an automated computational system. Instead of human experts manually analyzing and adjusting parameters, an automated system uses performance metrics and algorithms to substitute the manual tuning process, significantly reducing time requirements.
2Productivity
If settings are optimized for one hardware configuration, then performance is maximized for that configuration, but the settings are difficult to reuse on different configurations
Solution Approach 1:
The automated tuning system creates parameter settings that are universal across different hardware configurations. Instead of maintaining separate optimized settings for each hardware type, the system generates adaptable parameters that can be applied universally, allowing the same system to optimize performance across various hardware environments without requiring configuration-specific manual tuning.
Solution Approach 2:
The system implements dynamic parameter adjustment that adapts to different hardware configurations automatically. Rather than using static settings fixed for one hardware type, the parameters are designed to be dynamic and responsive, automatically adjusting to the specific hardware environment they are deployed on, thus achieving both optimization and adaptability.
3Reliability
If manual reconfiguration is performed for different hardware configurations, then performance can be optimized, but the process is costly and error-prone
Solution Approach 1:
The system performs self-service automated tuning that eliminates the need for manual reconfiguration processes. The automated system independently evaluates performance and adjusts parameters without human intervention, removing the complexity and error-prone nature of manual reconfiguration while maintaining reliable performance optimization.
Solution Approach 2:
The system implements continuous feedback loops that monitor performance metrics and automatically adjust parameters in response. This feedback mechanism replaces complex manual reconfiguration processes with automated closed-loop control, reducing complexity while ensuring reliable performance optimization through continuous monitoring and adjustment.
Data Source
AI summary
An approach is provided for generating graph database parameter settings. Parameter settings for importing data into a graph database are determined. A speed of importing simulated data into the graph database and a system resource usage are determined by executing an importing of the simulated data using the parameter settings and a simulated hardware environment. A reward associated with the parameter settings is determined. Using a policy network that includes convolutional neural networks and based on the reward and the settings, candidates of adjusted parameter settings are determined. Using a Monte Carlo tree search in multiple iterations to estimate changes in speeds of importing the simulated data and changes in system resource usages for candidates of the adjusted parameter settings, rewards for the candidates are determined. Based on the rewards, a candidate is selected as including final parameter settings that optimize an importing speed and system resource usage.