Network Node Data Collection for User-Perceived Anomalies
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Solution Overview
Problem
Existing IT management systems lack contextual relevance in analyzing computing performance anomalies from end-user perception, using arbitrary performance benchmarks that fail to consider rapidly changing conditions, leading to instability and performance fluctuations.
Innovation Solution
A computer network system that collects and stores contextually relevant computing metrics from local client computers and networks, correlating data with user-perceived computing performance anomalies, using a client-node system with processors for information collection, mapping, and storage, allowing for queryable and sortable data for optimization needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing IT management systems use arbitrary performance benchmarks and thresholds to optimize computing performance, then optimization is attempted, but the analysis lacks contextual relevance to end-user perception of computing performance quality
Solution Approach 1:
The system implements feedback by capturing end-user perceptions of computing performance quality and using this information to adjust and refine performance measurements. User feedback loops allow the system to continuously improve the relevance of its measurements by comparing arbitrary benchmarks against actual user-experienced quality, thereby reducing the loss of contextual information.
Solution Approach 2:
The system performs preliminary actions by proactively collecting and analyzing end-user perception data before formal performance optimization attempts. This preliminary gathering of contextual information ensures that subsequent performance measurements and optimizations are grounded in actual user experiences rather than arbitrary thresholds, preventing information loss from the outset.
2Loss of information
If the system collects comprehensive computing metrics from local client computers and networks, then contextual relevance is improved, but system complexity increases
Solution Approach 1:
The system applies universality by designing multi-functional components that simultaneously perform multiple tasks. For example, the data collection mechanism not only gathers computing metrics but also automatically correlates them with user perceptions, maps network connectivity paths, and identifies temporal patterns. This consolidation of functions into unified components reduces overall system complexity while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The system merges previously separate functions into integrated components. Data collection, correlation with user perceptions, network path mapping, and temporal analysis are combined into a unified system architecture. This merging eliminates the need for multiple independent systems, reducing complexity while preserving the ability to collect and analyze comprehensive computing metrics with contextual relevance.
3Measurement precision
If the system correlates collected data with timeframes temporally adjacent to user-perceived anomalies, then diagnostic accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-organizing and time-stamping collected metrics data as it is gathered, rather than performing extensive processing only when anomalies occur. This preliminary structuring of data with temporal context allows for more efficient subsequent analysis, reducing the computing power required during actual anomaly detection while maintaining high diagnostic accuracy through temporal correlation.
Solution Approach 2:
The system extracts and focuses only on the specific temporal window adjacent to user-perceived anomalies, rather than processing entire datasets. By isolating and extracting only the relevant time-frame data for correlation analysis, the system significantly reduces processing requirements while maintaining high diagnostic accuracy for the specific anomaly detection task.
4Adaptability or versatility
If the system provides configurable and scalable data collection, then adaptability to different network environments is improved, but device complexity increases
Solution Approach 1:
The system applies dynamics by implementing configurable parameters that can be dynamically adjusted based on specific network environments. Rather than requiring complex static configurations, the system allows runtime adjustment of data collection parameters, thresholds, and scope, enabling adaptability to different networks through simple dynamic changes rather than complex architectural modifications.
Solution Approach 2:
The system implements local quality by allowing different configuration parameters to be applied to different local contexts or network segments. Each client computer or network segment can have customized data collection settings appropriate to its specific environment, while sharing a common underlying architecture. This approach provides high adaptability without requiring complex system-wide reconfiguration.
Data Source
AI summary
A computer network system relating to collecting (at a user-node) network and computer information (temporally adjacent to a user-perceived computing performance anomaly) from connectivity resource nodes at a time determined by user action. The system provides a local configurable resource for iteratively collecting computing performance information relating to the quality of service experienced by a computer user interfacing with a computer network.