Directed-Graph Health Model for Automated Cloud Service Monitoring
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Solution Overview
Problem
Existing cloud resource monitoring systems struggle to provide a holistic, adaptable, and efficient health monitoring solution that correlates the health of individual resources to broader service health determinations, often leading to overwhelming alerts and manual analysis due to varying owner perspectives on health.
Innovation Solution
A directed graph health model is implemented to automatically correlate cloud resource health to broader service health, using anomaly detection and machine learning to determine health states, with customizable thresholds and actions based on owner-defined rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to determine service health, then health determination can be made, but it requires significant time and human resources
Solution Approach 1:
The system implements self-service by automatically correlating cloud resource health data to determine service health without requiring manual analysis. The automated system processes health data, applies correlation rules, and generates service health determinations independently, eliminating the need for human analysts to perform time-consuming manual assessments while maintaining accurate health determination.
2Loss of information
If comprehensive monitoring of all cloud resources is implemented, then complete health visibility is achieved, but alert noise increases significantly
Solution Approach 1:
The system merges individual cloud resource health data into unified service-level health determinations. By correlating resource health data according to service dependencies and aggregation rules, the system combines multiple granular health signals into consolidated service health views, maintaining complete health information while reducing alert noise through intelligent filtering and grouping of related alerts.
Solution Approach 2:
The system introduces service-level health determinations as intermediaries between individual cloud resource health status and end-user concerns. This intermediary layer aggregates and correlates resource health data, translating granular resource alerts into meaningful service-level insights, thereby maintaining complete health visibility while filtering out noise from individual resource fluctuations that don't impact overall service health.
3Productivity
If a standardized health model is applied across all entities, then scalability is improved, but adaptability to specific owner perspectives decreases
Solution Approach 1:
The system implements dynamics by making the health model configurable and adaptable to different owner perspectives while maintaining a standardized core structure. The model allows owners to customize correlation rules, thresholds, and service definitions according to their specific needs and perspectives on health, while the underlying standardized framework ensures scalability across multiple entities and owners.
4Adaptability or versatility
If detailed customization options are provided for health monitoring, then adaptability to owner perspectives is improved, but system complexity increases
Solution Approach 1:
The system applies local quality by allowing customization at specific local points in the health model where owners need adaptability, such as correlation rules and thresholds for their specific services, while maintaining standardized configurations for common scenarios. This approach provides detailed customization options where needed without requiring full system complexity, as the standardized portions remain unchanged and manageable.
Data Source
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AI summary
The techniques described herein automatically correlate the health of cloud resources to a broader health determination for an entity executing within, or supported by, a distributed computing environment. In contrast to the typical manual analysis that is required to make a broader health determination for a specific entity, the techniques generate and use a standard health model that can be applied, or scaled, to detect unhealthy scenarios across a variety of different entities with different owners (e.g., different tenants and/or different cloud resource providers). Furthermore, to meet varying owner perspectives on health, the techniques include a layer on top of the standard health model that enables an owner to provide input that customizes the standard health model for their own entity.