Event Scoring in Resource Networks for Root Cause Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for diagnosing root cause problems in IT infrastructure and other resource networks rely on manual inspection and domain knowledge, which can be time-consuming and inefficient, especially when dealing with large numbers of event alerts.
Innovation Solution
A computer-implemented method and system for scoring events as likely cause events in a resource network, involving the processing of incoming events to correlate related events, mapping these events onto a sub-topology graph representing resource relationships, and scoring each event based on a combination of event classification weights and path scores derived from relationship scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection and domain knowledge are used to diagnose root cause problems, then diagnostic accuracy can be achieved, but the Mean Time to Know (MTTK) increases significantly
Solution Approach 1:
The patent introduces an automated scoring system as an intermediary between event data and human operators. The system calculates likelihood scores based on topology relationships and event characteristics, serving as a mediator that prepares diagnostic recommendations before human review. This reduces the time operators need to spend on manual analysis while maintaining diagnostic accuracy through structured scoring methodologies.
Solution Approach 2:
The system performs preliminary analysis by automatically calculating likelihood scores for potential root causes before operators arrive at their conclusions. By pre-computing topology-based relationships and event correlation scores, the system prepares diagnostic candidates in advance, allowing operators to review pre-ranked possibilities rather than starting from scratch during incident response.
2Measurement precision
If operators manually identify problem graphs by inspecting network topology, then root cause diagnosis can be achieved, but the complexity of the process increases
Solution Approach 1:
The system enables self-service diagnosis by automatically computing likelihood scores and ranking potential root causes without requiring operators to manually trace topology relationships. The automated scoring mechanism independently evaluates event correlations and topology paths, producing ready-to-review diagnostic recommendations that reduce the cognitive burden on operators while maintaining accurate root cause identification.
Solution Approach 2:
The patent transforms the complex qualitative assessment of topology relationships into quantitative likelihood scores. By converting topological distance, event correlation strength, and causal relationship confidence into numerical parameters, the system simplifies the diagnostic process while preserving the ability to identify root causes accurately through score-based ranking.
3Reliability
If teams of engineers monitor the state of resources using management systems, then service status can be inferred, but the resource consumption and operational overhead increase
Solution Approach 1:
The patent replaces manual monitoring and analysis activities with automated computational processes. Instead of engineers manually inspecting management system data and inferring service status, the system automatically calculates likelihood scores based on topology relationships and event patterns, substituting human analytical effort with algorithmic processing that consumes fewer operational resources while maintaining monitoring accuracy.
Data Source
AI summary
Method and system are provided for scoring events as likely cause events in a resource network. The method processes incoming events relating to resources in a resource network to correlate related events as a related group and maps the events of the related group onto nodes of a sub-topology representing resources of the resource network and having edges representing relationships between the resources. The method scores each event by discovering one or more paths between the node event and other node events of the related group, with the scoring based on a combination of an event classification weight and a discovered path score of aggregated relationship scores of edges of a discovered path.