Dynamic Timeout Control for Web Applications
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Solution Overview
Problem
Existing application timeout solutions are static and pre-configured, leading to inefficiencies such as increased network traffic and resource utilization due to inactive users, causing delays for other users who need immediate responses, as they do not account for user interaction behavior or application criticality.
Innovation Solution
A personalized timeout control system that dynamically adjusts the hardcoded timeout parameter based on user behavior and application criticality using behavioral analytics and contextual analysis, implemented through a software development kit (SDK) that intercepts web-service requests to optimize resource usage and reduce waiting times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a hardcoded timeout parameter is used, then the application structure is simple and easy to maintain, but the system cannot adapt to different user behaviors and application criticality levels
Solution Approach 1:
The patent transforms the static hardcoded timeout parameter into a dynamic timeout parameter that automatically adjusts based on user behavior patterns and application criticality. The system continuously monitors user interaction and modifies the timeout value in real-time, making the timeout control mechanism adaptive rather than fixed.
Solution Approach 2:
The patent changes the timeout parameter from a fixed constant to a variable that can take different values based on determined user behavior and application criticality. This parameter transformation allows the system to optimize timeout settings dynamically without requiring complex reconfiguration.
2Loss of time
If the timeout parameter is extended for all users, then user waiting time increases, but network traffic and resource utilization increase due to inactive sessions
Solution Approach 1:
The patent applies different timeout values to different user sessions based on their specific behavior patterns and the criticality of their applications. Instead of a uniform timeout setting, each user experiences a customized timeout that matches their interaction style, allowing the system to terminate inactive sessions appropriately without affecting active users.
Solution Approach 2:
The system continuously monitors user behavior and uses this feedback to adjust timeout parameters in real-time. By observing user interaction patterns, the system can determine when a session has become inactive and adjust the timeout accordingly, preventing resource waste from prolonged inactive sessions while maintaining appropriate timeout periods for active users.
3Productivity
If the timeout parameter is reduced, then resource utilization improves, but users experiencing delays or critical operations may be prematurely terminated
Solution Approach 1:
The patent dynamically adjusts the timeout parameter based on application criticality determination. For critical applications, the system maintains longer timeout periods to ensure reliable operation completion, while for non-critical applications, it reduces the timeout to improve resource utilization. This selective parameter adjustment prevents premature termination of important operations.
Solution Approach 2:
The system dynamically adapts the timeout value based on real-time assessment of application criticality and user behavior. Rather than using a fixed reduced timeout, the system flexibly modifies the timeout period to match the actual needs of each application context, ensuring that critical operations are not prematurely terminated while still improving overall resource efficiency.
4Speed
If static timeout solutions are used, then system complexity is low, but parallel users experience delays due to resource occupation by inactive sessions
Solution Approach 1:
The system automatically monitors user behavior and manages timeout parameters without requiring manual intervention or complex external control mechanisms. The timeout management system is self-regulating, using observed user patterns to automatically adjust timeout values, thereby improving response speed for parallel users while keeping the overall system architecture relatively simple.
Data Source
AI summary
A computer-implemented method includes: determining, by a computer device, user behavior with an application; determining, by the computer device, a criticality of the application to a user; and changing, by the computer device, a hardcoded timeout parameter of the application into a dynamic timeout parameter based on the determined user behavior and the determined criticality of the application.


