Cloud Provisioning Error Handling via Service-Specific Classification
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Solution Overview
Problem
The labor-intensive process of provisioning cloud services in a cloud environment is complex and often manual, lacking effective failure handling mechanisms, which hampers efficiency and automation.
Innovation Solution
A method and system for handling failures in the execution flow of provisioning operations, involving error classification and corrective actions specific to each service, allowing for automated error management and recovery within the cloud infrastructure system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual provisioning processes are used, then flexibility and adaptability are maintained, but productivity and efficiency deteriorate due to labor-intensive operations
Solution Approach 1:
The system enables self-service provisioning by automatically detecting errors, classifying them by type, and executing appropriate corrective actions without human intervention. The error handling module autonomously manages the provisioning process, allowing the system to service itself and improve productivity while maintaining controlled complexity.
Solution Approach 2:
The provisioning system is segmented into distinct modular components: error detection module, error classification module, and corrective action execution module. This segmentation allows each component to handle specific tasks independently, improving overall provisioning efficiency while keeping the complexity manageable through modular design.
2Productivity
If automated error handling is implemented, then productivity improves, but device complexity increases due to additional error management mechanisms
Solution Approach 1:
The error classification module changes parameters by categorizing errors into different types (service-specific error types) and selecting appropriate corrective actions based on these classifications. This parameter-based approach streamlines error handling efficiency while managing complexity through systematic classification rather than complex decision logic.
Solution Approach 2:
The error classification type acts as an intermediary between error detection and corrective action execution. This intermediate classification layer simplifies the overall system by providing a standardized interface that maps diverse errors to specific remediation procedures, improving efficiency without proportionally increasing complexity.
3Reliability
If service-specific error classification is used, then reliability improves through targeted corrective actions, but measurement precision requirements increase for error identification
Solution Approach 1:
The system applies local quality by implementing service-specific error classification where each service type has its own error types and corrective actions tailored to its characteristics. This localized approach improves provisioning reliability for each service while managing detection complexity through service-specific rather than universal error handling.
Solution Approach 2:
The system performs preliminary classification of errors into service-specific types before executing corrective actions. This preliminary categorization step improves reliability by ensuring the right action is taken for each error type, while the classification framework itself manages the complexity of error detection through predefined categories.
4Loss of time
If manual error handling is performed, then ease of operation is maintained for complex errors, but loss of time increases due to manual intervention requirements
Solution Approach 1:
The system reduces error resolution time by implementing self-service error handling where the system automatically detects, classifies, and corrects errors without requiring manual operator intervention. This automation significantly cuts time loss while the standardized classification framework maintains operational simplicity.
Solution Approach 2:
The system implements feedback loops where error detection triggers automatic classification and corrective action execution. This closed-loop feedback mechanism reduces error resolution time by eliminating manual intervention steps, while the structured feedback process maintains ease of operation through systematic rather than ad-hoc handling.
Data Source
AI summary
Techniques for handling failures in the execution flow of provisioning operations for a computer infrastructure system are disclosed. The techniques can include determining a service type of a service that generated an error during provisioning. An error handler can be selected based on the service type.