Correlated Application Recommendation for Incident Resolution
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Solution Overview
Problem
The increasing complexity of computer systems and services makes it difficult to identify the cause of errors or incidents in application services, impact on other applications, and to take corrective actions in a timely manner.
Innovation Solution
A recommendation system using machine learning to learn correlations between applications based on historical and real-time contextual data, identifying potential root cause applications and impacted or correlated applications to resolve incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual troubleshooting methods are used to identify incident causes and impacted applications, then technical support agents can resolve incidents, but the process is time-consuming and requires extensive manual effort
Solution Approach 1:
The patent introduces an automated intermediary system that acts as a mediator between incident detection and resolution. This system automatically analyzes incident data, identifies root cause applications, determines impacted applications, and generates remediation recommendations, thereby eliminating the need for manual troubleshooting and significantly reducing incident resolution time
Solution Approach 2:
The system enables self-service by automatically performing troubleshooting tasks without human intervention. It autonomously correlates incident data with application dependency information, identifies affected applications, and provides remediation guidance, allowing the system to resolve incidents independently and improving overall productivity
2Measurement precision
If comprehensive analysis of application dependencies and incident data is performed, then accurate identification of root cause and impacted applications is achieved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing application dependency relationships and organizing incident data structures before incidents occur. This preparation work includes mapping application dependencies, storing historical incident data, and creating lookup tables, which enables rapid and accurate incident analysis without complex real-time computations
Solution Approach 2:
The patent segments the incident analysis process into distinct modular components: incident data reception, root cause identification, impacted application determination, and remediation recommendation. Each module handles a specific aspect of the analysis independently, reducing overall system complexity while maintaining comprehensive analysis capabilities
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for generating a set of correlated applications by a recommendation system to resolve an incident for an application service (AS). The recommendation system can determine an AS-to-AS similarity matrix based on incident data and contextual data, an AS-to-AS affinity matrix based on the incident data and contextual data, and generate a set of correlated applications based on the similarity matrix and the affinity matrix. The set of correlated applications can also be generated based on pairwise application associations, where an application association between a first application and a second application indicates that the first application and the second application can occur together in an incident or to be changed together as indicated by a change record.