Dynamic Timeout Prediction Using Contextual Weights
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Solution Overview
Problem
Static predefined timeouts in computer applications can lead to unnecessary user wait times or excessive errors due to being either too high or too low, as they do not adapt to dynamic network conditions.
Innovation Solution
A method for real-time dynamic timeout prediction, where context data from previous transactions is used to generate predictive weights and an offset, allowing for the estimation of load times and setting of timeouts that adapt to current conditions, ensuring optimal wait times based on network type, request type, and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static predefined timeout values are used, then the system is simple to implement, but the timeout accuracy is poor leading to unnecessary wait times or excessive errors
Solution Approach 1:
The system performs preliminary actions by storing context data from numerous past transactions and pre-calculating predictive weights and offsets before they are needed. When a new data request arrives, the system has already prepared the statistical model components (weights and offsets) that will be used to predict the timeout, eliminating the need for complex real-time calculations during the request handling.
Solution Approach 2:
The system creates a simplified copy of the complex timeout determination problem by using historical transaction data to generate representative weights and offsets. Instead of analyzing all possible transaction parameters in real-time, the system copies the essential timing characteristics from past transactions into pre-computed statistical parameters that can be quickly applied to new requests.
2Reliability
If timeout is set too high, then fewer false errors are generated, but user wait time increases unnecessarily
Solution Approach 1:
The system dynamically changes the timeout parameter based on the specific context of each data request. Instead of using a fixed timeout value, the system adjusts the timeout parameter by combining predictive weights with current transaction context and adding an offset derived from historical data, allowing the timeout to adapt to varying network conditions and transaction types.
Solution Approach 2:
The system incorporates feedback from historical transaction outcomes by storing context data from numerous past transactions and using it to generate predictive weights. The system learns from past performance patterns and uses this feedback to continuously improve timeout predictions, adjusting the timeout values based on what has worked well in similar situations before.
3Loss of time
If timeout is set too low, then user wait time is reduced, but the system generates excessive false errors
Solution Approach 1:
The system dynamically adjusts the timeout parameter based on the specific context of each data request, including the type of data being requested, network conditions, and historical performance data. This allows the timeout to be optimized for each individual transaction rather than using a one-size-fits-all approach.
Solution Approach 2:
The system applies different timeout characteristics to different types of data requests based on their specific requirements. By analyzing the context of each request and comparing it with historical data, the system tailors the timeout parameter to the local characteristics of that specific transaction type, ensuring appropriate wait times for different operations.
Data Source
AI summary
Embodiments of the present disclosure pertain to real time dynamic timeout prediction. In one embodiment, a method includes context data for numerous transactions are stored and used to generate predictive weights and an offset. Context for new transactions are used to predict load times, and the offset is used to produce a timeout.