Adaptive Cloud Session Idle Timeout Management
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Solution Overview
Problem
Existing cloud computing environments use a fixed idle timeout for all users, leading to inefficient resource usage and increased costs, as it may result in cloud computing sessions lasting longer than needed for some users, tying up resources that could be used for others.
Innovation Solution
A method to intelligently determine and adjust the idle timeout for each cloud computing session based on user and session attributes, using machine learning techniques to predict user activity and adaptively set the timeout, ensuring more accurate and efficient resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed timeout value is used for all cloud computing sessions, then the system is simple to manage and operate, but resource usage efficiency deteriorates and costs increase
Solution Approach 1:
The patent implements dynamic timeout values that automatically adjust based on user behavior patterns and session characteristics. The system transitions from a static fixed timeout to a dynamic adaptive timeout that changes in real-time, resolving the contradiction by making the timeout parameter flexible rather than rigid while maintaining automated management.
Solution Approach 2:
The system changes the timeout parameter from a fixed constant to a variable determined by multiple factors including user interaction history, session type, and activity patterns. This parameter transformation allows the timeout to adapt to different scenarios, improving resource efficiency while the automated determination process maintains operational simplicity.
2Device complexity
If a fixed timeout value is used for all cloud computing sessions, then the system configuration is simple, but resource allocation efficiency worsens
Solution Approach 1:
The system performs self-service by automatically determining appropriate timeout values without requiring manual configuration. The automated determination process analyzes user behavior and session attributes to set optimal timeouts, eliminating the need for complex manual configuration while achieving efficient resource allocation through adaptive timeout management.
Solution Approach 2:
The system implements feedback loops that continuously monitor user activity and adjust timeout values accordingly. By gathering data on user behavior patterns and using this feedback to dynamically adjust timeouts, the system achieves efficient resource allocation automatically, resolving the contradiction between configuration simplicity and resource efficiency.
3Ease of manufacture
If cloud computing sessions are terminated after a fixed timeout, then resource reclamation is straightforward, but session duration becomes suboptimal for some users
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns and predicting optimal timeout values before the session ends. By pre-determining appropriate timeout values based on observed activity patterns and session characteristics, the system ensures sessions are terminated at optimal times without requiring complex real-time decision-making during termination.
Data Source
AI summary
Systems and techniques for determining an idle timeout for a cloud computing session are described. An example technique includes determining a first one or more attributes associated with a user of the cloud computing session and determining a second one or more attributes associated with an operation of the cloud computing session. An idle timeout for the cloud computing session is determined, based at least in part on the first one or more attributes and the second one or more attributes. User activity is monitored during the cloud computing session. Upon determining, based on the monitoring, an absence of the activity of the user within a duration of the idle timeout, the cloud computing session is terminated.