Network Management System Root Cause Identification
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Solution Overview
Problem
Current network management systems struggle to accurately identify and address issues within cloud-based application sessions, as they rely solely on application performance data, which is insufficient to determine underlying network-related causes of performance problems.
Innovation Solution
A network management system (NMS) that combines application performance data with network data to reactively determine and proactively predict issues, using machine learning models to identify network features contributing to failures and impact performance, and invokes actions to remedy or prevent these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If application performance data alone is used to diagnose issues, then the diagnostic process is simple, but the accuracy of identifying root causes is insufficient
Solution Approach 1:
The patent combines application performance data with network device data into a unified data set. This merging allows the system to correlate application-level metrics (latency, throughput, packet loss) with network device metrics (CPU utilization, memory usage, interface statistics) to accurately identify root causes of performance issues without requiring complex separate analysis systems.
Solution Approach 2:
The patent introduces a network management system as an intermediary that collects, correlates, and analyzes both application performance data and network device data. This intermediary component simplifies the overall system architecture by centralizing the data processing function and providing a single point of analysis rather than requiring direct integration between multiple independent systems.
2Reliability
If reactive issue determination is performed, then the system can identify past problems, but it cannot predict future issues
Solution Approach 1:
The patent implements predictive analytics that analyze historical network data and performance patterns to forecast future issues before they occur. By performing preliminary analysis of trends in data collection, the system can predict potential performance degradation or failures and trigger proactive remediation actions, reducing the time loss associated with reactive problem-solving.
Solution Approach 2:
The patent establishes a feedback loop where the results of predictive analytics are used to adjust and improve future predictions. The system continuously monitors predicted issues, compares them with actual outcomes, and uses this feedback to refine its predictive models, thereby improving reliability over time while optimizing the time required for analysis through learned patterns.
3Productivity
If manual analysis of network data is performed, then the process is easy to understand, but the productivity of issue resolution is low
Solution Approach 1:
The patent implements automated root cause analysis that performs data collection, correlation, and diagnosis without requiring manual intervention. The system automatically correlates application performance data with network device data, identifies performance issues, determines root causes, and even executes remediation actions, enabling the network management system to serve itself and dramatically improving issue resolution productivity.
Solution Approach 2:
The patent replaces manual analytical processes with automated computational systems. Instead of human analysts manually examining network data and performance metrics, the system uses automated algorithms and machine learning models to process data, identify patterns, and determine root causes, substituting mechanical human analysis with automated computational analysis to enhance productivity.
4Measurement precision
If comprehensive network data is collected from multiple sources, then the accuracy of issue diagnosis improves, but the device complexity increases
Solution Approach 1:
The patent implements a universal data collection framework that can gather diverse data types from multiple sources (application performance metrics, network device statistics, flow data) through a single standardized interface. This multi-functional approach allows the system to accurately diagnose performance issues by integrating comprehensive network data while managing complexity through a unified data collection mechanism that handles different data sources consistently.
Data Source
AI summary
A network management system (NMS) is described that determines a cause or contributor of an issue of an application session or predicts an issue with the application session and invokes one or more actions to remedy or prevent the issue. NMS is configured to determine one or more network features that cause and/or contribute to an issue of an application session that has already occurred (referred to herein as “reactive issue determination”) and/or predict an issue with an application session and one or more network features that impact the performance of the application session (referred to herein as “predictive issue determination”), and invoke one or more actions to remedy or prevent the issue, in accordance with one or more techniques of this disclosure.