Machine-Learning Cloud Outage Detection and Classification
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Solution Overview
Problem
Current systems in cloud computing environments inefficiently detect and mitigate service outages and software regressions, leading to inefficient use of resources and prolonged troubleshooting.
Innovation Solution
Anomaly detection server using machine learning models to analyze telemetry data, classify errors, and automatically initiate mitigation actions based on error type and impact severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual log analysis is used to detect and classify errors, then engineers can identify error causes, but the process consumes excessive time and system resources
Solution Approach 1:
The system enables self-service error detection and classification by automatically analyzing telemetry data, classifying errors into types (code defects, service outages, benign errors), and determining impact severity without human intervention. This automated self-diagnosis eliminates the need for manual log analysis while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual log analysis with an automated computational system that uses machine learning models and algorithms to detect, classify, and prioritize errors. This substitution transforms the error detection process from a manual, time-consuming operation to an automated, rapid analysis system.
2Reliability
If all errors are monitored and analyzed equally, then comprehensive error detection is achieved, but system resources are wasted on low-impact errors
Solution Approach 1:
The system applies local quality by differentiating error analysis depth and resource allocation based on error type and impact severity. Critical errors (service outages, code defects) receive immediate, comprehensive analysis while benign errors (404s, connection refused) are automatically filtered or given minimal attention. This selective approach optimizes resource distribution across different error categories.
Solution Approach 2:
The patent changes the parameter of error prioritization by introducing impact severity as a key classification parameter. Errors are evaluated and ranked based on multiple parameters including error type, frequency, affected users, and system impact, allowing the system to focus resources on high-severity errors while maintaining comprehensive monitoring capability.
3Productivity
If automated error classification is implemented, then detection speed increases, but system complexity increases
Solution Approach 1:
The system segments the error detection and classification process into distinct modular components: telemetry data collection, machine learning model analysis, error classification engine, impact assessment module, and mitigation action system. This segmentation allows each component to be independently optimized, maintained, and scaled, managing overall system complexity through modular architecture.
Data Source
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AI summary
Systems and methods for automatically detecting and mitigating errors in a cloud computing environment. One example method includes receiving, from a telemetry server, telemetry data for the cloud computing environment, detecting an error within the cloud computing environment based on the telemetry data, determining an error type for the error based on the telemetry data, determining an impact severity for the error based on the telemetry data, and when the error type is a reportable error type and the impact severity exceeds a predetermined threshold, performing a mitigation action.