AIOPs System for Automated Operational Event Response
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Solution Overview
Problem
Current methods for responding to operational events, such as errors or outages in computing systems, involve manual reporting by users, which often results in incomplete and inaccurate records of actions taken during the event resolution.
Innovation Solution
A computer-implemented method that includes receiving notifications of operational events, obtaining and providing recommendation actions to users, capturing user actions and system responses, and generating final outcome reports with timestamps, utilizing real-time data and machine learning models to enhance response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually create reports for operational events, then the reports can be generated with user insight and context, but the reports often fail to include a complete and accurate account of the user's actions
Solution Approach 1:
The system implements feedback by automatically capturing user actions during operational event response and using this captured data to generate outcome reports. The AIOPs system continuously monitors user interactions with the computing system, captures these actions automatically, and feeds this information back into the report generation process, ensuring complete and accurate recording of all user actions without relying on manual documentation.
Solution Approach 2:
The system applies self-service by enabling automatic capture and documentation of user actions without requiring manual intervention. The AIOPs system autonomously monitors, records, and generates reports of operational event responses, eliminating the need for users to manually document their actions while ensuring complete and accurate information capture.
2Reliability
If users search through various error logs and systems to identify actions during operational events, then comprehensive information can be gathered, but the response time and efficiency are reduced
Solution Approach 1:
The system implements preliminary action by pre-configuring the AIOPs system to automatically monitor and capture all relevant user actions and system states during operational events. This preliminary setup ensures that when an operational event occurs, the system is already positioned to automatically record all necessary information without requiring users to search through logs or manually gather data, thus maintaining both completeness and speed.
Solution Approach 2:
The system replaces the mechanical process of manual information gathering with an automated electronic monitoring and capture system. The AIOPs system uses software-based automatic capture mechanisms to record user actions, eliminating the need for users to manually search through error logs and systems, thereby maintaining information completeness while dramatically reducing response time.
3Adaptability or versatility
If manual reporting is used for operational events, then users can provide contextual understanding, but the productivity and automation level of the system remains low
Solution Approach 1:
The system replaces manual reporting mechanisms with automated electronic capture and generation systems. The AIOPs system automatically monitors user actions, captures relevant information, and generates outcome reports without manual intervention, thereby dramatically improving productivity and report generation efficiency while maintaining adaptability through AI-driven analysis of captured data.
Solution Approach 2:
The system enables self-service by allowing the AIOPs system to autonomously perform the entire report generation process. The system automatically captures user actions, analyzes the captured data using AI algorithms to extract contextual understanding, and generates comprehensive outcome reports without requiring user involvement, thus maximizing productivity while preserving adaptability.
4Adaptability or versatility
If AIOPs systems are trained with manually created reports, then the training data can include user insights, but the training accuracy is compromised due to incomplete information
Solution Approach 1:
The system implements feedback by using automatically captured action data to train the AIOPs system. The continuous feedback loop captures actual user actions during operational events, uses this accurate data to train and refine the AI models, and improves training accuracy by eliminating the inaccuracies inherent in manual reporting while preserving the adaptability gained from real user behavior data.
Data Source
AI summary
Methods and systems for responding to an operational event are provided. Aspects include receiving a notification of the operational event, obtaining a first set of recommendation actions for responding to the operational event, and providing the first set of recommendation actions to a user responding to the operational event. Aspects also include capturing a first response action performed during responding to the operational event, obtaining, based at least in part on the first response action, a second set of recommendation actions for responding to the operational event, displaying the second set of recommendation actions based to the user responding to the operational event, capturing a second response action performed during responding to the operational event, and generating a final outcome report of the operational event including the first response action, the second response action, and an automatically captured timestamp of the first response action and the second response action.