Distributed Network Snapshot Analysis for Root Cause Resolution
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Solution Overview
Problem
Current systems for identifying and resolving network anomalies in distributed networks rely on historical data or replication, resulting in incomplete and obfuscated root cause analysis.
Innovation Solution
A system that uses real-time snapshots to capture network data, extracts key values, compares them to expected values, and iteratively resolves anomalies using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical data or replication methods are used to identify anomalies, then the system can detect anomalies, but the root cause analysis becomes incomplete and obfuscated
Solution Approach 1:
The system proactively captures real-time snapshots of the distributed network environment when anomalies are detected, preserving the actual state of the system at the time of the anomaly. This preliminary action ensures that critical context information is captured before it may be lost or changed, enabling complete root cause analysis.
Solution Approach 2:
Instead of relying on historical data or replication attempts, the system creates accurate copies (snapshots) of the actual network environment at the moment of anomaly detection. These snapshots include all relevant data, configurations, and states, providing a faithful reproduction of the anomaly context for analysis.
2Productivity
If manual analysis of network anomalies is performed, then detailed investigation is possible, but the process is time-consuming and inefficient
Solution Approach 1:
The system automatically captures snapshots, extracts key values, compares them against expected values, and identifies anomalies without requiring manual intervention. This self-service approach enables the system to autonomously perform detailed investigations, significantly reducing the time from anomaly detection to root cause identification.
Solution Approach 2:
The system replaces manual mechanical analysis with automated computational processes. Machine learning models and automated comparison algorithms substitute for human analysts, rapidly processing snapshot data and identifying root causes without the time constraints of manual investigation.
3Measurement precision
If comprehensive network data is captured for analysis, then accurate anomaly identification is achieved, but computational resources increase
Solution Approach 1:
The system extracts only the key values and critical data elements from the comprehensive network snapshot that are necessary for anomaly identification. By focusing on essential information rather than processing all captured data, the system maintains high detection accuracy while reducing computational resource consumption.
Solution Approach 2:
The system captures comprehensive snapshots but processes only the necessary portions (key values) for anomaly detection. This partial processing approach avoids the excessive computational burden of analyzing every detail while still achieving accurate anomaly identification through targeted analysis of critical parameters.
Data Source
AI summary
Systems, computer program products, and methods are described herein for identifying and resolving anomalous data within a distributed network, the system configured to detect an event within the distributed network, wherein the event comprises at least one anomaly within the distributed network, identify an application(s) associated with the event, apply data associated with at least one event to a centralized platform, control whether an identified application initiates the capture of at least one real-time snapshot, initiate a real-time snapshot process, extract a key value(s) from each real-time snapshot, compare each extracted key value(s) to an expected value for each corresponding key value, identify the anomaly within the distributed network by identifying data which does not conform to the expected value for each extracted key value, and resolve the at least one anomaly within the distributed network when each extracted key value conforms to the expected value for each key value.