Database Synchronization Using ML Workload Adaptation
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Solution Overview
Problem
Current methods for applying changes in target database systems are inefficient due to fixed configurable parameters that do not adapt to changing workloads, leading to latency issues and suboptimal replication times.
Innovation Solution
A data synchronization system using a trained machine learning model to dynamically adjust configurable parameters based on the current workload level, selecting the most appropriate application algorithm for each change to minimize replication time and maintain low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed configurable parameters are used for applying changes in the target database system, then the system structure is simple and easy to implement, but the replication efficiency deteriorates and latency increases under varying workload conditions
Solution Approach 1:
The patent implements dynamic parameter adjustment by training a machine learning model to predict optimal configurable parameters based on current workload conditions. The system transitions from static fixed parameters to dynamic adaptive parameters that automatically adjust to changing database workload levels, thereby improving replication efficiency without requiring complex manual intervention
Solution Approach 2:
The patent changes the state of configurable parameters from fixed to variable by using machine learning predictions. The trained model analyzes workload patterns and dynamically modifies parameter values such as batch sizes, commit frequencies, and application algorithms, allowing the system to optimize replication performance across different operational conditions
2Adaptability or versatility
If fixed configurable parameters are used for applying changes, then implementation is straightforward, but latency increases and adaptation to changing workloads is poor
Solution Approach 1:
The system implements self-service by autonomously adjusting its own configurable parameters through the trained machine learning model. The model continuously monitors workload conditions and automatically modifies parameters without external intervention, enabling the database synchronization system to adapt to changing workloads independently and improve latency performance
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning model continuously receives information about current workload conditions, replication performance metrics, and latency measurements. This feedback loop enables the system to learn from past performance and dynamically adjust parameters to optimize adaptability across varying operational scenarios
3Loss of time
If dynamic parameter adjustment using machine learning is implemented, then replication efficiency improves and latency reduces, but the device complexity and computational overhead increase
Solution Approach 1:
The patent applies preliminary action by training the machine learning model offline or during low-workload periods before deployment. The pre-trained model contains learned patterns and optimal parameter configurations that can be quickly applied during high-workload scenarios, reducing the computational burden during critical replication operations and minimizing latency
Data Source
AI summary
The present disclosure relates to a computer implemented method for applying changes into tables of a target database system using a data synchronization system, the data synchronization system being configured to automatically use at least one configurable parameter for applying a requested change in the target database system. The method provides a trained machine learning model, the machine learning model being configured to adjust the at least one configurable parameter based on a workload level. The method determines a current workload level at the target database system and uses the machine learning model for adjusting the at least one configurable parameter according to the determined workload level.