Dynamic Server Connection Timeout Adjustment in Cloud
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Solution Overview
Problem
Server connection timeouts in cloud environments are inefficient as they do not provide descriptive information about the cause of errors, leading to unnecessary waiting and resource wastage, and existing solutions fail to dynamically adjust timeouts based on contextual situations.
Innovation Solution
A method that creates a knowledge corpus of connection timeout patterns through historical learning, predicts the criticality of transactions, dynamically adjusts connection timeouts, analyzes recent timeouts, and outputs a recommended reinitiation timeline to optimize server connection timeouts in cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed predetermined timeout range is used for server connections, then the system can prevent devices from waiting ceaselessly, but it causes unnecessary resource wastage and provides no descriptive error information
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed predetermined timeout range to a dynamic timeout mechanism that adjusts the timeout value based on contextual situations. The system evaluates factors such as transaction criticality, historical timeout patterns, and current system state to selectively identify appropriate timeout ranges for different transactions, thereby optimizing the balance between preventing ceaseless waiting and minimizing unnecessary resource wastage.
Solution Approach 2:
The patent changes the timeout parameter from a static fixed value to a dynamic value that varies based on contextual evaluation. The system creates a knowledge corpus of timeout patterns and uses this to selectively identify timeout ranges, effectively changing the parameter state from constant to variable based on situational factors.
2Reliability
If a fixed predetermined timeout range is used for server connections, then the system can prevent devices from waiting ceaselessly, but it does not provide descriptive information about the cause of errors
Solution Approach 1:
The patent implements feedback by creating a knowledge corpus that stores timeout patterns and contextual information from historical transactions. When a timeout occurs, the system evaluates the contextual situation and provides descriptive error information based on this feedback loop, enabling better error diagnosis while maintaining reliable timeout handling.
Solution Approach 2:
The patent performs preliminary action by creating and maintaining a knowledge corpus of timeout patterns before actual timeout events occur. This pre-computed knowledge base enables the system to quickly evaluate contextual situations and provide descriptive error information when timeouts happen, rather than reacting without context.
3Reliability
If the connection timeout is extended to allow more waiting time, then critical transactions may complete successfully, but non-critical transactions waste more resources and time
Solution Approach 1:
The patent applies local quality by evaluating and applying different timeout characteristics to different transactions based on their specific contextual situations. Instead of a universal timeout value, the system selectively identifies timeout ranges tailored to each transaction's criticality and context, ensuring critical transactions receive extended timeouts while non-critical ones use shorter timeouts for optimal resource efficiency.
Data Source
AI summary
In an approach to optimize server connection timeout errors in a cloud environment, embodiments create a knowledge corpus associated with connection timeout patterns based on historical learning of transaction parameters and predicts a criticality of a transaction based on one or more identified contextual situations. Further, embodiments dynamically adjust a connection timeout range of the transaction based on the predicted criticality and one or more identified contextual situations of the transaction, and selectively identify a connection timeout range for the transaction based on an evaluation of the one or more contextual situations. Additionally, embodiments analyze generated timeout errors on a remote server from within a service mesh, and adjust timeout values of the transaction based on the analyzed generated timeouts errors. Responsive to the transaction receiving a timeout error, embodiments output a recommended timeline detailing when the transaction can be reinitiated.