AI Observability Engine for Cross-Stack Alert Correlation
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Solution Overview
Problem
Current monitoring tools are limited to linear alerts within a single technology stack and struggle to correlate alerts across different technology stacks, leading to inefficiencies and extended Mean Time to Restore (MTTR) in complex operational environments with multiple systems and operational teams.
Innovation Solution
An observability engine that receives alerts from heterogeneous systems, identifies correlated alerts, and uses an AI model to determine a root cause, generating a comprehensive alert that contextualizes the issue across multiple systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional monitoring tools are used to monitor alerts from multiple heterogeneous systems, then each system can be monitored individually, but the tools fail to correlate alerts across different technology stacks and require manual intervention
Solution Approach 1:
The patent introduces a standardized data format and correlation engine as an intermediary layer between heterogeneous monitoring systems. This intermediary translates diverse alert formats into a unified structure, enabling cross-stack correlation without requiring complex custom integrations for each system pair. The standardized format acts as a mediator that simplifies the correlation process while maintaining adaptability across different technology stacks.
Solution Approach 2:
The correlation engine is designed with universal functionality to handle multiple types of alerts from different technology stacks through a single unified interface. The standardized data format enables the engine to process various signal types (network, application, infrastructure alerts) using the same correlation logic, making the system multi-functional rather than requiring separate correlation mechanisms for each alert type.
2Measurement precision
If fully qualified signal data with complete configuration information is used for correlation, then accurate correlation can be achieved, but the data processing becomes more complex and resource-intensive
Solution Approach 1:
The signal data is segmented into essential correlation fields and optional detailed fields. The standardized data format separates critical information needed for correlation (such as alert ID, timestamp, system identifier) from supplementary configuration details. This segmentation allows the correlation engine to process only the essential fields for basic correlation while maintaining the option to incorporate additional fields when needed, reducing routine processing complexity while preserving accuracy.
Solution Approach 2:
The patent transforms raw alert data into a standardized parameter structure with defined fields and data types. By changing the parameter representation from unstructured or semi-structured format to a standardized schema, the system achieves consistent and accurate correlation. The parameter transformation occurs once during ingestion, after which the standardized parameters can be efficiently processed without repeating complex parsing operations.
3Loss of time
If manual correlation of alerts is performed, then detailed analysis can be conducted, but the Mean Time to Restore (MTTR) is extended due to manual intervention requirements
Solution Approach 1:
The correlation engine operates autonomously to perform alert correlation, root cause identification, and incident grouping without requiring manual operational intervention. The system self-services by automatically ingesting alerts from multiple sources, correlating them based on the standardized data format, and presenting unified incident views. This automation eliminates manual correlation tasks while maintaining high accuracy, directly reducing MTTR and improving operational efficiency.
Solution Approach 2:
The system performs preliminary correlation and analysis actions automatically as alerts are received, before operational teams need to intervene. By pre-processing and grouping related alerts into unified incidents in advance, the system prepares structured information ready for operational review, eliminating the need for manual correlation work and accelerating the response timeline.
Data Source
AI summary
Provided are systems and methods that facilitates cross-correlation among alerts within different systems in a complex operating environment. In one example, a method may include receiving a plurality of alert messages generated by a plurality of systems within a distributed and shared operating environment and storing the plurality of alert messages, identifying a subset of alert messages among the plurality of alert messages that are correlated based on relationships identified from the subset of alert messages, generating a description of a root cause of the subset of alert messages based on execution of an artificial intelligence (AI) model on the identified subset of alert messages, and displaying the description of the root cause via a user interface.