Cloud Deployment Correlation for Failure Attribution
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Solution Overview
Problem
In cloud infrastructure, detecting and correlating failures caused by multiple deployed components is challenging due to the highly-coupled nature of infrastructure components, leading to noisy failure signals with varied latency, making it difficult to pinpoint issues promptly and potentially causing significant downtime and profit loss.
Innovation Solution
A method and system for correlating failure events with deployment events to render decisions on whether to automatically stop a problematic component, involving correlation scoring, attribution, and binary decision-making using historical data and regression models to identify impacted parts of the cloud infrastructure for diagnostic purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If components are continuously updated to fix or enhance features, then productivity and feature enhancement are improved, but system reliability deteriorates due to potential deployment failures causing downtime
Solution Approach 1:
The system performs preliminary actions by monitoring failure signals and correlating them with deployment events before the full deployment takes effect. When a failure pattern is detected, the system can roll back or halt the deployment proactively, preventing widespread impact. This is achieved through continuous collection of failure signals, deployment signals, and health metrics, with correlation analysis performed in advance of complete deployment.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring failure signals, deployment signals, and health metrics, then using this information to adjust deployment decisions. The correlation analysis provides feedback about the relationship between deployments and failures, enabling the system to learn from past deployments and make informed decisions about future deployments, thereby balancing continuous improvement with reliability.
2Measurement precision
If failure detection is performed broadly across the cloud infrastructure, then measurement precision of failure correlation is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system segments the monitoring approach by focusing on specific failure signals and deployment events rather than attempting to monitor every possible system parameter. It segments the correlation analysis into discrete time windows and uses thresholds to identify significant patterns. This segmentation reduces complexity while maintaining precision by concentrating resources on the most critical failure modes and deployment relationships.
Solution Approach 2:
The system changes parameters dynamically by adjusting monitoring intensity and correlation thresholds based on the current state of the cloud infrastructure. It can modify the time windows for correlation analysis, adjust sensitivity thresholds for failure detection, and adapt the scope of monitoring based on identified risk patterns. This dynamic parameter adjustment allows the system to maintain high measurement precision while reducing complexity during normal operation.
3Reliability
If failure signals are monitored with high sensitivity to detect early impacts, then reliability of failure detection is improved, but false positives increase due to noisy signals from multiple sources
Solution Approach 1:
The system merges multiple monitoring signals (failure signals, deployment signals, health metrics) into a unified correlation analysis framework. By combining these signals and analyzing their temporal and contextual relationships, the system can distinguish true failure patterns from isolated noise. The merged analysis uses correlation thresholds and time window comparisons to filter false positives while maintaining sensitivity to genuine issues.
Solution Approach 2:
The system introduces correlation analysis as an intermediary layer between raw failure signals and deployment decisions. This intermediary process contextualizes failure signals by comparing them against deployment events and historical patterns, filtering out false positives caused by unrelated noise. The correlation analysis acts as a mediator that translates raw signals into actionable insights about deployment impact.
4Productivity
If deployment is rolled out broadly to maximize feature coverage, then productivity is improved, but the impact radius of potential failures increases causing more significant downtime
Solution Approach 1:
The system performs preliminary monitoring and correlation analysis during the deployment process itself, before the full rollout completes. By detecting failure patterns early in the deployment cycle, the system can halt or roll back the deployment proactively, limiting the impact radius to only the affected segment rather than the entire user base. This enables broad feature coverage attempts while maintaining safety nets.
Solution Approach 2:
The system implements dynamic deployment control that can adjust the rollout scope in real-time based on monitored failure signals. If failures are detected in a specific segment, the system can dynamically limit further deployment to that segment while continuing to roll out to other segments. This dynamic adjustment maintains overall productivity while containing the impact radius of failures to minimize downtime.
Data Source
AI summary
Methods, systems and computer program products are described for obtaining deployment signals comprising information relating to deployments of software components to a plurality of computing devices, obtaining fault signals comprising information relating to faults encountered by the plurality of computing devices, and obtaining device type information that describes a device type of each of the plurality of computing devices. Based on the deployment signals, fault signals, and device type information, a correlation score for each combination of software component, fault, and device type is obtained. Based on the correlation scores, attribution level decisions, fault type level decisions and device type level decisions are made. Based on these decisions, an overall decision is rendered whether to proceed or not proceed with a future deployment of the software component.