ML Event Clustering for Distributed System Root Cause Identification
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Solution Overview
Problem
In distributed computing systems, existing management tools struggle to timely identify the root cause of performance problems amidst numerous tangentially related events, overwhelming users with alerts and making it difficult to distinguish actual problem incidents from indirect indicators.
Innovation Solution
The implementation of computer-implemented methods and systems that classify runtime problem incidents by retrieving and analyzing historical events, constructing feature vectors, clustering, and identifying representative events to correlate with historical problem incidents, allowing for the application of remedial measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If management tools monitor and report all events in a distributed computing system, then complete visibility of system events is achieved, but users are overwhelmed by numerous alerts making it difficult to identify root cause problems
Solution Approach 1:
The patent segments the overwhelming stream of events into distinct temporal clusters using unsupervised machine learning. Events that occur within a defined time window are grouped together, allowing the system to present manageable clusters rather than individual events. This segmentation enables users to analyze grouped events representing complete problem incidents without being overwhelmed by the full volume of individual alerts.
Solution Approach 2:
The patent introduces an intermediary machine learning system that sits between the event monitoring tools and users. This intermediary automatically performs clustering, root cause identification, and temporal correlation analysis, transforming raw events into meaningful problem incident groups. The ML system acts as a mediator that filters and structures information before presenting it to users, eliminating the need for users to manually sort through all individual alerts.
2Speed
If management tools report all events in rapid succession, then timely notification of problems is achieved, but users cannot timely sort through events to identify root problem incidents
Solution Approach 1:
The patent applies preliminary action by performing event clustering and root cause analysis automatically as events occur, rather than waiting for users to manually analyze them later. The unsupervised machine learning system pre-processes events in real-time, grouping related events and identifying potential root causes before users need to investigate. This preliminary automated analysis eliminates the time users would otherwise spend sorting through events manually.
Solution Approach 2:
The patent replaces the mechanical manual process of event sorting and analysis with an automated machine learning system. Instead of users manually examining and categorizing events, the ML system automatically performs temporal clustering, event correlation, and root cause identification. This substitution of automated intelligence for manual mechanical analysis dramatically reduces the time required to identify root cause problems while maintaining rapid notification capabilities.
3Reliability
If many alerts are generated for tangential problem incidents, then comprehensive monitoring is achieved, but the actual root problem incident becomes buried and difficult to identify
Solution Approach 1:
The patent implements feedback through iterative unsupervised machine learning that continuously refines event clustering based on temporal patterns and event relationships. The system learns from the structure and timing of events, automatically adjusting cluster formations to better separate root cause events from tangential incidents. This feedback-driven approach improves the precision of root cause identification while maintaining comprehensive monitoring of all system events.
Solution Approach 2:
The patent changes the parameters of event analysis by introducing temporal clustering parameters and unsupervised learning parameters. Instead of treating all events equally, the system applies time-based grouping parameters that consider the temporal relationships between events. This parameter transformation allows the system to distinguish root cause events from tangential incidents based on their temporal patterns, improving measurement precision while maintaining comprehensive monitoring coverage.
Data Source
AI summary
Methods and systems are directed to discovering problem incidents in a distributed computing system. Events corresponding to historical problems incidents for the distributed computing system are retrieved from a data base. Sets of representative events of the various historical problem incidents for the distributed computing system are determined. A runtime problem incident in the distributed computing system is characterized by runtime events. The runtime problem incident is classified as corresponding to a historical problem incident of the historical problem incidents based on the runtime events and the sets of representative events. Remedial measures used to correct the historical problem incident may be used to correct the runtime problem.