Dependency Instance Health Analysis for Cloud Outage Root Cause
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Solution Overview
Problem
Identifying the root cause of a service outage in cloud computing environments is time-consuming due to the complexity of dependencies between services and the lack of granular monitoring of service dependency instances.
Innovation Solution
A system and method that analyzes the health statuses of service dependency instances using service level indicators (SLIs) to determine the root cause of a dependent service outage by identifying dependencies, instances, and their health statuses, providing a report on possible causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If service owners define thousands of metrics for monitoring dependent service and service dependency health, then monitoring coverage is improved, but finding the root cause of service outage becomes difficult and time consuming
Solution Approach 1:
The patent segments the monitoring data by distinguishing between dependent services and their service dependencies. It further segments dependencies into individual instances, allowing root cause analysis to focus on specific dependency instances rather than analyzing all thousands of metrics across the entire service landscape. This segmentation enables targeted investigation of outage causes.
Solution Approach 2:
The patent introduces service level indicators (SLIs) as intermediaries that aggregate and summarize the health status of service dependency instances. These SLIs serve as a mediator between the raw metric data and root cause analysis, providing a simplified view that highlights problematic dependencies without requiring direct analysis of all underlying metrics.
2Measurement precision
If granular monitoring of service dependency instances is implemented, then outage detection precision is improved, but system complexity increases
Solution Approach 1:
The system segments monitoring into hierarchical levels: dependent services, service dependencies, and individual dependency instances. This segmentation allows granular monitoring of specific instances without requiring simultaneous monitoring of all possible metrics at all levels, reducing overall system complexity while maintaining detection precision.
Solution Approach 2:
The patent applies local quality by monitoring service dependency instances with higher granularity only when necessary for root cause analysis. Instead of uniformly applying high-granularity monitoring across all services and dependencies, the system focuses detailed monitoring on specific instances that are suspected of causing outages, based on SLI data and dependency relationships.
Data Source
AI summary
Examples of the present disclosure describe systems and methods determining a root cause of an outage of a dependent service. A method includes detecting an outage of a dependent service, determining a first service dependency of the dependent service, and identifying one or more instances of the first service dependency by accessing a service provider of the first service dependency. The method also includes collecting one or more service level indicators (SLIs) for one or more instances of the first service dependency and determining a health status of the instances of the first service dependency using the SLIs. The method further includes determining a root cause for the outage of the dependent service based on the health status of the instances of the first service dependency.