Service Deployment Infrastructure Request Provisioning Automation
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Solution Overview
Problem
The complexity of provisioning resources in cloud infrastructure systems leads to labor-intensive and error-prone processes, particularly due to the manual handling of intensive processing tasks and the need for fine-grained error identification and handling techniques.
Innovation Solution
A Service Deployment Infrastructure (SDI) is implemented to automate the provisioning, management, and tracking of services, enabling request-level and task-level control for executing, rolling back, retrying, or failing tasks, with error handling processes that include error type and resource type-based policies for retry, delay, or rollback operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual processing is used for provisioning cloud services, then flexibility and adaptability are maintained, but labor intensity and error rates increase significantly
Solution Approach 1:
The error handling system is segmented into multiple specialized modules including error detection module, error classification module, error handling policy module, and rollback module. Each module handles a specific aspect of error management, dividing the complex automated provisioning process into manageable segments that can be independently developed and maintained.
Solution Approach 2:
An intermediary error handling system is introduced between the automated provisioning processes and the underlying infrastructure. This intermediary layer detects, classifies, and manages errors without requiring manual intervention, acting as a mediator that enables automation while handling complexity internally.
2Measurement precision
If fine-grained error identification and handling techniques are implemented, then error detection accuracy improves, but system complexity and labor intensity increase
Solution Approach 1:
The error handling system implements local quality by applying different handling strategies and policies to different types of errors and different stages of the provisioning process. Error detection thresholds, retry policies, and rollback procedures are customized locally based on the specific error context, resource type, and task criticality rather than using a uniform approach throughout.
Solution Approach 2:
The system changes parameters dynamically based on error characteristics. Detection sensitivity, handling policies, and retry intervals are adjusted as parameters based on the classified error type and context. This allows high detection accuracy for critical errors while maintaining system simplicity through parameterized configurations rather than hard-coded complex logic.
3Reliability
If automated error handling with retry and rollback policies is implemented, then service reliability improves, but processing time and system complexity increase
Solution Approach 1:
Error handling policies including retry limits, rollback procedures, and escalation rules are predetermined and configured before errors occur. These preliminary actions are stored as policies that are automatically executed when errors are detected, eliminating the need for real-time decision-making and reducing processing time during actual error events.
Solution Approach 2:
The error handling system operates autonomously through self-service mechanisms. The automated error handling module detects errors, classifies them, selects appropriate policies, and executes retry or rollback operations without external intervention. This self-service capability improves reliability by ensuring consistent error handling while minimizing the time loss associated with manual error management.
Data Source
AI summary
In certain embodiments, a Service Deployment Infrastructure (SDI) request engine is disclosed. The SDI request engine performs the tracking, management and provisioning of services subscribed to by customers of the cloud infrastructure system. The SDI request engine is deployed to process large volumes of provisioning requests and deliver time critical applications for customers. The SDI request engine translates each request into a list of tasks of various sizes based on the requirement and configuration of the request. In some embodiments, the SDI request engine imposes control and management on both request and task levels in order to execute, rollback, retry or fail a task automatically and accurately.