Network Resource Regulation via Session Data Matching
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Solution Overview
Problem
Cloud providers face challenges in determining which network resources to regulate and how to regulate them effectively to mitigate data-transmission issues such as slow speeds or transmission errors, as the cause of these problems may be unclear.
Innovation Solution
The method involves searching for prior-session data that matches current-session data to identify the cause of transmission degradation and initiating corrections based on this identification, using a state machine to analyze network parameters and build a database of problem causes for future sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of transmission problems is performed to identify network resources to regulate, then accuracy in identifying the correct resource can be improved, but time consumption and operational complexity increase
Solution Approach 1:
The system enables automatic self-diagnosis and self-correction by comparing current session parameters against historical problem patterns stored in the database. The automated identification engine matches current transmission degradation patterns with previously analyzed sessions, automatically identifying the affected network resources and initiating corrective actions without manual intervention.
Solution Approach 2:
The system performs preliminary analysis by pre-processing and storing problem patterns, causes, and corrective actions in a database during normal operation. When transmission degradation occurs, the system queries this pre-prepared knowledge base to rapidly identify matching patterns and apply proven solutions, avoiding the need for time-consuming real-time manual analysis.
2Reliability
If comprehensive network parameters are monitored and analyzed to identify transmission problems, then problem detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex network monitoring task into distinct functional modules: parameter collection from multiple network elements, pattern matching engine that compares current sessions with historical data, database storage for problem patterns, and automated correction execution. Each module handles a specific aspect of the problem, making the overall complex system manageable and maintainable.
Solution Approach 2:
The system introduces an intermediary pattern-matching engine that acts as a mediator between raw network parameters and problem identification. This intermediary layer processes and compares session parameters against stored problem patterns, transforming complex multi-parameter data into identifiable transmission problems without requiring direct complex analysis of all parameters simultaneously.
3Productivity
If automated systems are implemented to regulate network resources, then response speed is improved, but initial setup and configuration complexity increase
Solution Approach 1:
The automated system performs self-configuration by automatically collecting network parameters, analyzing transmission problems, and building the problem pattern database without extensive manual setup. Once the database is populated with historical problem data, the system autonomously identifies transmission degradation patterns and executes corrective actions, minimizing the need for ongoing configuration intervention.
Solution Approach 2:
The system implements feedback loops where the results of automated corrections are fed back into the database, continuously improving the accuracy of pattern matching. The system learns from each transmitted problem and correction applied, refining its ability to quickly and accurately respond to future transmission issues while maintaining automated operation.
Data Source
AI summary
Systems and methods are provided for regulating network resources to improve data-transmission quality. In one implementation, a method includes searching one or more computer-readable memory locations for prior-session data that matches current-session data. The prior-session data may comprise information describing a prior data-transmission session and the current-session data may comprise information describing a current data-transmission session. Data describing a cause of transmission degradation may be stored in association with the prior data-transmission session. The method may also include identifying, by at least one processor, the cause of transmission degradation in the prior data-transmission session as being a cause of transmission degradation in the current data-transmission session if the prior-session data matches the current-session data. The method may also include initiating correction of the cause of transmission degradation based on the identification.