Machine Learning Timer Control for Distributed Transaction Queues
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed computing systems face inefficiencies due to statically defined values that do not adapt to changing conditions, leading to suboptimal performance in processing data transactions.
Innovation Solution
A machine learning system using a trained neural network dynamically adjusts the duration of timers in a distributed transaction processing system based on changing conditions, allowing each identifier to have its own time duration adjusted accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static timer values are used in a distributed transaction processing system, then system simplicity is maintained, but system adaptability and execution quality deteriorate because the timer cannot respond to changing conditions
Solution Approach 1:
The patent implements dynamic timer values that automatically adjust based on real-time system conditions such as transaction volume, processing speed, and resource availability. The timer transitions from a static configuration to a dynamic parameter that continuously adapts to changing operational environments, resolving the contradiction between adaptability and complexity through automated adjustment mechanisms.
Solution Approach 2:
The system incorporates feedback loops that monitor system performance metrics and use this information to adjust timer values. By continuously measuring transaction processing outcomes and feeding this data back to the timer control mechanism, the system achieves adaptability without requiring complex manual configuration, as the feedback automatically guides parameter optimization.
2Productivity
If dynamic timer values are implemented to respond to changing conditions, then execution quality and system efficiency improve, but system complexity increases due to the need for continuous monitoring and adjustment mechanisms
Solution Approach 1:
The timer system performs self-adjustment by automatically monitoring its own performance metrics and modifying its values without external intervention. This self-service capability allows the system to optimize its own efficiency while minimizing the complexity burden on operators, as the timer autonomously responds to changing conditions based on predefined performance thresholds and adjustment algorithms.
Solution Approach 2:
The patent changes the timer from a fixed parameter to a variable parameter that adjusts its value based on system conditions. By implementing parameter changes that are driven by performance metrics rather than manual configuration, the system achieves improved productivity while keeping complexity management automated, reducing the need for complex control interfaces and manual tuning mechanisms.
3Reliability
If static configuration values are used throughout the system, then ease of operation is maintained, but system responsiveness and execution quality worsen due to inability to adapt to real-time conditions
Solution Approach 1:
The system performs preliminary configuration of timer adjustment rules and performance thresholds before operation begins. By pre-establishing the logic for how timer values should change under different conditions, the system achieves high execution quality and reliability while maintaining ease of operation, as operators only need to configure high-level parameters rather than manage complex real-time adjustments.
Data Source
AI summary
Dynamic timers are determined using machine learning. The timers are used to control the amount of time that new data transaction requests wait before being processed by a data transaction processing system. The timers are adjusted based on changing conditions within the data transaction processing system. The dynamic timers may be determined using machine learning inference based on feature values calculated as a result of the changing conditions.