Runbook Operation Recommendations via Event Attribute Analysis
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Solution Overview
Problem
Current runbook generation processes lack efficient recommendations for runbook operations, leading to suboptimal diagnosis and remediation of IT system issues, as they do not effectively utilize user activity data and event attributes to suggest relevant operations during the runbook creation process.
Innovation Solution
A system that recommends runbook operations by analyzing user activity data and event attributes, using a machine learning model trained on historical runbook data to identify correlations and suggest candidate operations for inclusion in the runbook, thereby enhancing the diagnostic and remedial capabilities of the runbook generation interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated runbook generation is implemented without operation recommendations, then runbook creation speed is improved, but diagnosis and remediation accuracy deteriorates
Solution Approach 1:
The system performs preliminary analysis of user activity data and event attributes before runbook generation to identify relevant operations. By pre-processing this data and preparing operation recommendations in advance, the system ensures that accurate diagnostic operations are suggested without delaying the runbook creation process, thus resolving the contradiction between creation speed and diagnosis accuracy
Solution Approach 2:
The patent replaces manual selection of diagnostic operations with an automated machine learning model that analyzes user activity and event data. This substitution of mechanical/manual processes with intelligent automation provides accurate operation recommendations instantly, maintaining high runbook creation speed while ensuring diagnostic precision through data-driven insights
2Reliability
If manual selection of runbook operations is used, then operation relevance is improved, but runbook generation time increases
Solution Approach 1:
The system enables self-service by automatically analyzing user activity data and event attributes to generate relevant operation recommendations without requiring manual intervention. The machine learning model autonomously identifies pertinent operations based on historical patterns and current events, ensuring operation relevance while eliminating the time cost of manual selection
Solution Approach 2:
The system incorporates feedback loops where user interactions with recommended operations and actual runbook performance data are continuously analyzed. This feedback mechanism refines the machine learning model's ability to recommend relevant operations, improving reliability over time while maintaining efficient automated generation without manual review
Data Source
AI summary
Techniques for recommending runbook operations during a runbook generation process are disclosed. A system recommends operations for including in the runbook based on attributes of a detected event. The system presents event attributes associated with the detected event to a user in a runbook generation interface. When the user selects an event attribute, the system presents a set of candidate runbook operations associated with the event attribute. Based on user selections of runbook operations to include in a runbook, the system generates and stores a template of the runbook in which the attribute values associated with the detected event are omitted. Upon execution of the runbook at a future time, the system, or a user, may populate the event attributes in the runbook with values that correspond to the future event of the same event type.