Cloud Job Deployment Validation for Policy Accuracy
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Solution Overview
Problem
Current methods for deploying updates in cloud-based services do not adequately consider the scope and risk of updates, leading to inadequate and incorrect deployment policies, and lack safeguards for ensuring complete and accurate job definitions.
Innovation Solution
Implementing build time validation rules that require definition of scope and risk attributes for jobs, with additional requirements such as authorization for high-risk or global scope jobs, to ensure appropriate deployment policies are created and enforced.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deployment policies are determined based on user input without validation, then the deployment process is simple and quick, but errors and missing data lead to inadequate and incorrect deployment policies
Solution Approach 1:
The system performs validation of job information before the deployment process begins. Build-time validation rules check for required attributes (scope, risk level) and data quality before deployment policies are determined, preventing errors from propagating through the deployment process.
Solution Approach 2:
A validation component acts as an intermediary between job creation and deployment policy determination. This component enforces validation rules, ensures data completeness, and coordinates between different subsystems to maintain data quality without adding significant complexity to the overall process.
2Reliability
If deployment policies do not consider scope and risk attributes, then the deployment process is faster and simpler, but the policies are inadequate for high-risk or large-scale updates
Solution Approach 1:
The system applies different validation rules and deployment policies based on the specific attributes of each job. High-risk or global-scope jobs trigger additional validation requirements and more stringent deployment policies, while lower-risk jobs follow standard procedures, optimizing both safety and efficiency.
Solution Approach 2:
The validation system dynamically adjusts deployment requirements based on job parameters such as scope and risk level. When high-risk attributes are detected, the system automatically implements additional validation steps and modifies deployment policies accordingly, rather than applying a one-size-fits-all approach.
3Productivity
If manual processes are used to coordinate updates across multiple subsystems with different deployment configurations, then flexibility is maintained, but the deployment process becomes time-consuming and error-prone
Solution Approach 1:
The validation component serves multiple functions: it validates job information, determines appropriate deployment policies, coordinates across different subsystems, and ensures consistency. This multi-functional approach streamlines the deployment process while maintaining reliability across diverse system configurations.
Solution Approach 2:
The system implements feedback mechanisms where validation results and deployment policy determinations are communicated back to the deployment process. This ensures that all subsystems receive consistent, validated information and can coordinate their actions accordingly, improving both speed and reliability.
Data Source
AI summary
Systems and methods for creating and deploying jobs in a cloud-based service include receiving a job definition defining job attributes pertaining to a job to be deployed in a cloud-based service. The job definition is processed to determine whether the job definition complies with predefined rules for job creation in the cloud-based service. Based on the determination of whether the job definition complies with the predefined rules for job creation in the cloud-based server, the job may be validated or not validated in the cloud-based service. Deployment policies are determined for validated jobs based on the job definition for the validated job.