ML-Based Dynamic Timers for Distributed Transaction Processing
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 ticker 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 execution quality and responsiveness deteriorate due to inability to adapt to changing conditions
Solution Approach 1:
The patent applies dynamics by transitioning from static timer values to dynamic timer values that automatically adjust based on real-time system conditions. The system monitors metrics such as transaction volume, processing speed, and queue depth, then dynamically modifies timer durations to optimize performance. This resolves the contradiction by making the timer system adaptive without requiring complex manual intervention or reconfiguration.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors transaction processing conditions and uses this information to adjust timer values. The feedback loop collects data on system performance metrics, analyzes current load conditions, and automatically modifies timer durations accordingly. This enables the system to adapt to changing conditions while maintaining manageable complexity through automated control.
2Productivity
If static processing times are configured, then configuration simplicity is maintained, but productivity deteriorates due to suboptimal performance under varying loads
Solution Approach 1:
The system dynamically adjusts processing times based on real-time transaction volume and system load. During high-volume periods, timer values are reduced to accelerate processing and minimize wait times. During low-volume periods, timer values are increased to allow thorough processing without unnecessary haste. This dynamic adjustment optimizes productivity while minimizing time loss under varying conditions.
Solution Approach 2:
The patent changes the parameter of timer duration based on system conditions. The system monitors transaction processing metrics and adjusts the timer parameter dynamically - shortening timers when processing capacity is available and extending them when the system is under heavy load. This parameter adaptation directly improves transaction processing efficiency while reducing unnecessary wait times.
3Reliability
If dynamic timer adjustment is implemented, then execution quality improves through real-time optimization, but device complexity increases due to additional control mechanisms
Solution Approach 1:
The system implements self-service by enabling automatic timer adjustment without external intervention. The control mechanism monitors system conditions and autonomously modifies timer values based on predefined performance criteria. This self-adjusting capability improves execution quality while managing complexity through automation, eliminating the need for complex manual control systems or continuous human oversight.
Solution Approach 2:
The patent uses feedback loops to maintain execution quality while controlling complexity. The system continuously monitors transaction processing outcomes and uses this feedback to automatically adjust timer values. The feedback mechanism ensures that timer adjustments are made only when necessary and in the direction that improves performance, preventing unnecessary complexity while maintaining high execution quality through data-driven decisions.
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.