Outage Risk Detection Alerts Using Reinforcement Learning
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Solution Overview
Problem
Current outage risk detection systems face challenges in accurately and efficiently identifying potential outages across multiple organizations due to noise from common incidents and lack of real-time information from external service providers, leading to delayed detection and increased false positives.
Innovation Solution
The implementation of a reinforcement learning-based outage risk detection model that receives historical feedback data to adjust weights for computer services associated with incidents, improving the model's fidelity and enabling earlier detection of outages by prioritizing services with confirmed past issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional outage risk detection systems monitor all computer services uniformly, then comprehensive coverage is achieved, but noise from common incidents increases and false positives increase
Solution Approach 1:
The patent applies local quality by assigning different weights to different computer services based on their historical significance and impact on organizations. Instead of uniform monitoring, the system identifies and prioritizes specific services that have caused outages in the past, giving them higher monitoring weights. This selective approach reduces noise from common incidents while maintaining comprehensive coverage of critical services.
2Measurement precision
If reinforcement learning is used to adjust service weights based on historical feedback, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously receives feedback data about actual outages and uses this information to adjust the weights of computer services through reinforcement learning. The feedback loop allows the model to learn from historical outcomes and improve its detection accuracy over time, automatically adapting to changing patterns without requiring manual reconfiguration.
3Reliability
If all computer services are monitored with equal weight, then complete monitoring coverage is maintained, but detection speed for critical outages decreases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and assigning weights to computer services based on their historical impact and significance before outages occur. The reinforcement learning model continuously updates these weights based on feedback data, so when an outage occurs, the system is already primed to detect and prioritize critical services faster. This pre-positioning of detection resources eliminates delays in identifying high-impact outages.
Data Source
AI summary
Feedback data corresponding to a historical system outage is received. One or more computer services are identified based on the feedback data. A weight for at least one of the one or more computer services is generated where the at least one of the one or more computer services is associated with an incident corresponding to the feedback data. An outage risk detection model is adjusted based on the weight generated for the at least one of the one or more computer services. A system outage is identified using the outage risk detection model.