Data Center Build Dependency Graph for Root Cause Tracking
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Solution Overview
Problem
Conventional tools for building data centers require significant manual effort and suffer from inaccuracies and usability issues due to the limited nature of capabilities in tracking progress and identifying blockages during region builds.
Innovation Solution
A new construct called 'skills' is introduced to manage operations, which are associated with skill metadata for execution order and health status, allowing for a dependency graph to be generated and monitored, along with alarms to identify the cause of health states and provide notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional tools are used for building data centers, then manual effort can be applied flexibly, but operational overhead increases and accuracy decreases
Solution Approach 1:
The system automatically monitors health status of skills, generates dependency graphs, and identifies root causes without requiring manual intervention. The orchestration system self-manages the tracking and analysis of build progress, eliminating the need for manual overhead while maintaining high accuracy through automated dependency resolution and health monitoring.
2Measurement precision
If capabilities are used to track region build progress, then basic availability can be monitored, but accuracy in identifying blockages decreases
Solution Approach 1:
The system segments the region build process into discrete skills with defined dependencies, allowing precise tracking of each component's health status. By breaking down the complex build process into manageable skill units, the system achieves high measurement precision in identifying blockages while maintaining ease of operation through structured, modular monitoring.
3Productivity
If manual methods are used for root cause analysis, then flexibility in investigation is maintained, but time to identify errors increases
Solution Approach 1:
The system continuously monitors health status of skills and provides real-time feedback through the dependency graph, automatically identifying root causes when issues occur. This feedback mechanism enables rapid error identification by immediately tracing dependencies and highlighting the source of problems, dramatically reducing the time required for root cause analysis compared to manual methods.
4Ease of operation
If conventional orchestration tools are used, then basic task coordination is achieved, but overhead for managing dependencies increases
Solution Approach 1:
The dependency graph dynamically adapts to the specific build scenario, automatically adjusting to show only relevant dependencies and health statuses. This dynamic visualization reduces the perceived complexity by presenting information adaptively based on current build state, making dependency management easier while handling complex relationships in the background.
Data Source
AI summary
Skills and skills metadata may be used to define a process for building a data center. Skills of one service may depend on skills corresponding to the same or different service. A dependency graph may be generated based on these dependencies. The graph may specify an order by which orchestration operations are to be performed to build the services, thereby building the data center. During execution of the process for building the data center, health states corresponding to the skills may be tracked (based at least in part on alarms and/or namespaces associated with the skills). When an unhealthy skill is identified, the system may traverse the dependency graph to identify a root cause (e.g., failed operations corresponding to a skill on which the unhealthy skill directly/indirectly depends). A notification and/or various options may be provided to address the unhealthy state of one or both skills.