Network Diagnostic System for Alert Condition Identification
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Solution Overview
Problem
Service providers face challenges in efficiently identifying and addressing network performance issues in triple play services, as existing methods are time-consuming and costly due to complex network maintenance and difficulty in pinpointing the cause of degraded performance or network failures.
Innovation Solution
A computer-implemented method and system that receive diagnostic data from network interface devices, analyze it to identify performance alert conditions, and generate outputs to help service providers quickly identify and troubleshoot issues by gathering and analyzing data from multiple network interface devices and user locations, using a computing device to determine potential causes and their probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diagnostic data is collected from multiple network interface devices and analyzed to identify performance alert conditions, then the precision of network issue detection is improved, but the complexity of the diagnostic system increases
Solution Approach 1:
The diagnostic system is segmented into multiple independent network interface devices, each with its own diagnostic module that collects and analyzes data locally. This distributes the diagnostic functionality across the network rather than requiring a centralized complex system, thereby improving detection precision through multiple data points while managing system complexity through modular design.
Solution Approach 2:
The diagnostic modules are designed to be universal and multi-functional, capable of collecting various types of diagnostic data (signal quality, error rates, throughput) from different network interface devices using standardized protocols. This universality allows the same diagnostic approach to be applied across diverse network equipment, improving detection precision without requiring device-specific complex diagnostic systems.
2Reliability
If comprehensive diagnostic data is gathered from multiple network interface devices, then the reliability of network performance monitoring is improved, but the amount of data to be processed increases
Solution Approach 1:
The system extracts only the most relevant diagnostic data elements from each network interface device, focusing on key performance indicators such as signal quality metrics, error rates, and throughput statistics. By extracting only essential data rather than collecting all possible information, the system maintains high monitoring reliability while managing data volume through selective data extraction.
Solution Approach 2:
The diagnostic system implements partial monitoring by selectively analyzing specific network parameters and devices based on predefined criteria and alert thresholds. Rather than continuously monitoring all aspects of every network interface device, the system performs targeted diagnostic actions only when certain conditions are met, thereby maintaining reliability through comprehensive monitoring where needed while reducing overall data processing volume.
3Ease of operation
If the system analyzes diagnostic data to identify potential causes and their probabilities, then the ease of troubleshooting is improved, but the computational resources required increase
Solution Approach 1:
The system performs preliminary analysis of diagnostic data by establishing baseline performance metrics and alert thresholds in advance. Diagnostic modules continuously compare current measurements against these pre-established criteria, enabling quick identification of anomalies without requiring complex real-time computational analysis. This preliminary action approach improves troubleshooting ease by providing immediate alert generation while minimizing ongoing computational resource consumption.
Solution Approach 2:
The diagnostic system implements self-service capabilities where network interface devices automatically collect, analyze, and report their own diagnostic data without requiring external intervention. The system autonomously identifies performance deviations and generates alerts, reducing the need for heavy centralized computational resources while improving troubleshooting ease through automated self-diagnosis and reporting.
Data Source
AI summary
A method includes receiving diagnostic data at a computing system from network interface devices. The method includes analyzing the diagnostic data with the computer system to identify a performance alert condition. The method includes determining, by the computer system, potential causes of the performance alert condition. The method includes determining, by the computer system, probabilities associated with the potential causes being actual causes of the performance alert condition. The method also includes generating, by the computer system, an output including a potential causes list ordered according to the probabilities associated with the potential causes being the actual causes of the performance alert condition.