Server Communication Change Detection Using Sub-User CUSUM Signals
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Solution Overview
Problem
Existing systems struggle to accurately detect changes in network communication between servers, especially when the output distribution of processes is unknown, making it difficult to identify when and if a change has occurred.
Innovation Solution
A computer-implemented system that includes a user server and a platform server, which detects sub-user data, calculates sub-user parameters, and uses a modified noise factor to enhance sensitivity, determining a condition of communication by comparing cumulative sum values with preselected thresholds, and feeds back the detected condition to the user server for corrective action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a change detection system is implemented to identify changes in network communication, then the reliability of network monitoring is improved, but the difficulty of detecting and measuring increases when the output distribution is unknown
Solution Approach 1:
The system performs preliminary actions by collecting historical sub-user data and calculating baseline parameters (mean, standard deviation, noise factor) before actual change detection occurs. This preliminary characterization of normal behavior enables the system to detect deviations more easily, resolving the contradiction by preparing detection capabilities in advance rather than attempting detection without prior knowledge
Solution Approach 2:
The system implements feedback by continuously monitoring sub-user data, comparing it against established parameters, and adjusting detection thresholds based on cumulative sum calculations. The feedback loop allows the system to adapt to changing conditions while maintaining reliable detection, addressing the difficulty of measuring changes in unknown distributions through iterative refinement
2Measurement precision
If sensitivity to certain data values is increased using a modified noise factor, then the measurement precision of change detection is improved, but the device complexity increases
Solution Approach 1:
The system applies parameter changes by modifying the noise factor based on the characteristics of sub-user data (mean, standard deviation). This dynamic parameter adjustment allows the system to optimize measurement precision for different data conditions without requiring completely different detection mechanisms, thereby improving precision while controlling complexity through a unified adaptable framework
Data Source
AI summary
Apparatuses, methods, and systems for determining a condition of a network are disclosed. One method includes receiving the sub-user data over a period of time, determining sub-user parameters including a mean, and a standard deviation of the sub-user data, determining a modified noise factor based on a value of the sub-user data, calculating a value of deviation from expectation based at least on the mean, the standard deviation, and the modified noise factor, calculating a current cumulative sum value of the sub-user data based on a prior cumulative sum value and the value of the deviation from expectation, comparing the current cumulative sum value with a preselected threshold, detecting a condition of a state of communication between the user server and the platform server based on the comparison, and feeding the condition of the state of communication back to the user server when the condition is detected.