Chaos Engineering Correlation for Network Root Cause Analysis
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Solution Overview
Problem
The increasing complexity of enterprise networks, spanning multiple domains and requiring multiple network management systems, leads to overwhelming amounts of telemetry, alarms, and events, making it difficult for Network Operation Center (NOC) teams to diagnose issues efficiently, as expert knowledge is often undocumented and in flux.
Innovation Solution
A chaos engineering tool is used to perform randomized actions in the network, collecting telemetry data and computing correlations to determine the root cause of events, enabling automated root cause analysis and reducing the mean time to resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple network management systems are deployed to manage complex enterprise networks, then network coverage and functionality are improved, but the complexity of diagnosing network issues increases and the amount of telemetry data becomes overwhelming
Solution Approach 1:
The patent combines telemetry data from multiple network management systems into a unified analysis platform. The system correlates events across different network domains (wireless, wired, data center, cloud) by computing relationships between events from disparate sources, effectively merging the functionality of multiple NMS into a single root cause analysis system.
Solution Approach 2:
The patent introduces an intermediary correlation system that sits between multiple network management systems and the analysis team. This intermediary computes correlations between events from different sources, translating the complex multi-system telemetry into actionable root cause identification without requiring direct human analysis of all underlying systems.
2Measurement precision
If expert knowledge is relied upon to diagnose network issues, then analysis quality is improved, but the process becomes dependent on individual personnel and slows down resolution
Solution Approach 1:
The patent enables the system to perform self-service root cause analysis by automatically computing correlations between events and identifying root causes without human intervention. The correlation engine autonomously processes telemetry data, determines causal relationships, and identifies root causes, eliminating dependence on individual expert knowledge while maintaining high analysis quality.
Solution Approach 2:
The patent performs preliminary correlation computations and event relationship mapping in advance, so that when network issues occur, the system can quickly identify root causes by comparing current events against pre-established correlation patterns. This preliminary processing of event relationships enables rapid root cause identification without requiring real-time expert analysis.
3Ease of manufacture
If traditional event correlation methods are used, then implementation simplicity is maintained, but the ability to identify root causes in complex multi-domain networks deteriorates
Solution Approach 1:
The patent segments the root cause analysis process into distinct computational steps: event collection from multiple sources, correlation computation between events, pattern recognition, and root cause identification. This segmentation allows the complex analysis task to be broken down into manageable computational operations that can be implemented systematically across multi-domain networks.
Solution Approach 2:
The patent changes the analytical parameters from simple event counting to multi-dimensional correlation computation. The system evaluates temporal relationships, spatial relationships across network domains, and causal relationships between events, transforming the analysis from basic event monitoring to sophisticated multi-parameter correlation that accurately identifies root causes in complex networks.
Data Source
AI summary
In one embodiment, a device initiates, using a chaos engineering tool, performance of randomized actions in a network via which an online application is accessible. The device obtains telemetry data from the network. The device computes correlations between the telemetry data and the randomized actions. The device uses the correlations to determine a root cause of an event in the network with respect to the online application.