Cloud Datacenter Orchestration Pipelines With Automated Failure Retries
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Solution Overview
Problem
The orchestration of datacenters on cloud platforms is complex, requiring manual steps prone to errors and security violations, leading to downtime and inefficiencies, especially in multi-tenant systems with diverse cloud platform requirements.
Innovation Solution
Implementing automated techniques for end-to-end and incremental orchestration of datacenters using cloud platform-independent declarative specifications, which automatically check and wait for execution dependencies to be completed before executing orchestration workflows, reducing manual intervention and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration steps are used for datacenter orchestration, then flexibility in handling diverse cloud platform requirements is maintained, but errors and security violations increase leading to downtime
Solution Approach 1:
The system performs self-validation of orchestration configurations against security policies and platform requirements automatically, without requiring manual review. The validation engine autonomously detects errors and prevents deployment of non-compliant configurations, eliminating the need for manual configuration checks while improving reliability.
Solution Approach 2:
Manual configuration and validation processes are replaced with an automated validation engine that uses policy definitions and platform requirement specifications to automatically verify orchestration configurations. This substitution eliminates human error while maintaining the ability to handle diverse cloud platform requirements through configurable policy rules.
2Productivity
If automated orchestration workflows are implemented, then efficiency and speed are improved, but handling of execution dependencies and errors requires complex retry mechanisms
Solution Approach 1:
The system pre-defines retry policies with specific parameters (maximum retry attempts, backoff intervals, timeout thresholds) before orchestration execution begins. These pre-configured policies are stored as part of the workflow definition, allowing the automated retry mechanism to operate without complex runtime decision-making, thereby maintaining high speed while managing complexity through advance preparation.
Solution Approach 2:
The retry mechanism dynamically adjusts execution parameters such as retry counts, wait intervals, and timeout values based on the specific orchestration task and observed error patterns. By parameterizing the retry behavior rather than hardcoding complex logic, the system achieves high productivity with manageable complexity through flexible parameter configuration.
3Productivity
If parallel execution of orchestration functions is enabled, then build efficiency is improved, but coordination of execution dependencies becomes more difficult
Solution Approach 1:
The orchestration workflow is divided into independent, modular tasks with clearly defined input and output contracts. Each task can be executed in parallel when dependencies are satisfied, and the system tracks completion status of each segment independently. This segmentation allows efficient parallel execution while simplifying dependency coordination through standardized interfaces between segments.
Solution Approach 2:
The system continuously monitors the execution status of parallel tasks and dynamically adjusts the scheduling of dependent tasks based on real-time feedback. When a prerequisite task completes successfully, the system immediately triggers the next dependent task without waiting for all parallel tasks to finish, optimizing build efficiency while maintaining correct dependency coordination through continuous state monitoring.
4Manufacturing precision
If comprehensive validation of execution dependencies is performed, then orchestration accuracy is improved, but validation time and overhead increase
Solution Approach 1:
The system performs validation of execution dependencies selectively based on the orchestration task type and risk level. For routine, low-risk operations, the system validates only critical path dependencies to minimize overhead. For complex or high-risk orchestrations, comprehensive validation is performed. This partial validation approach maintains high orchestration accuracy for critical operations while reducing overall validation time through intelligent prioritization.
Solution Approach 2:
Execution dependency validation rules and platform requirements are pre-compiled into validation policies before orchestration execution. The system caches validated dependency relationships and reuses them for subsequent similar operations, avoiding redundant validation work. This preliminary preparation maintains high orchestration accuracy while significantly reducing validation time through memoization of validation results.
Data Source
AI summary
Techniques are disclosed relating to implementing automated retries during orchestration of a datacenter on a cloud platform. Generating an orchestration workflow for the datacenter may include generating an aggregate pipeline for the orchestration. The aggregate pipeline includes instances of datacenter entity pipelines that include stages for provisioning and deployment of datacenter entities. The disclosed techniques include adding retry stages to the datacenter entity pipelines that are automatically invoked in the event of failure of a datacenter entity pipeline. The retry stages are placed at the end of individual datacenter entity pipelines and conditional expressions are included that invoke retry strategies defined by owners of the datacenter entity.