Pipeline Template Compilation for Cloud Deployment
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Solution Overview
Problem
Managing software releases on cloud platforms is complex due to the need to deploy different versions of software on various cloud computing resources, with existing continuous delivery platforms being time-consuming and error-prone, especially for multi-tenant systems with numerous data centers and services.
Innovation Solution
A system that uses cloud platform-independent declarative specifications to generate cloud platform-specific data centers and pipelines, allowing for the deployment of software artifacts across multiple cloud platforms with version management and rollback capabilities, while tracking changes and maintaining version pointers for efficient software release management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional continuous delivery platforms are used to manage software releases on cloud platforms, then software deployment can be performed, but the process becomes time-consuming and error-prone due to the complexity of managing multiple versions across numerous data centers and services
Solution Approach 1:
The patent segments the monolithic pipeline management approach into hierarchical levels (organization-level, data center-level, service-level). Each level has its own pipeline templates that can be independently managed and compiled. This segmentation allows parallel processing of pipeline compilations across different levels, reducing overall deployment time while maintaining accuracy through localized error handling and validation at each segment.
Solution Approach 2:
The patent implements preliminary compilation of pipeline templates into deployment packages before actual software releases. Version pointers are established in advance to reference specific pipeline template versions. This preliminary action allows the system to pre-validate pipeline configurations and prepare deployment packages, reducing errors during actual deployment and enabling faster execution since the heavy compilation work is done beforehand.
2Adaptability or versatility
If multiple pipeline templates are maintained for different versions of software on different cloud computing resources, then version-specific deployment is enabled, but tracking changes and identifying the exact modification that resulted in deployment issues becomes cumbersome and error-prone
Solution Approach 1:
The patent implements feedback mechanisms through version pointers that track which pipeline template versions are currently deployed. When deployment issues occur, the system can trace back through the version pointer history to identify exactly which template version and which specific modifications caused the problem. This feedback loop enables precise change detection and facilitates targeted debugging without requiring manual inspection of multiple pipeline versions.
Solution Approach 2:
The patent creates copyable deployment packages that encapsulate specific pipeline template versions. These packages can be replicated and distributed across multiple data centers and services consistently. When issues arise, the copied packages can be analyzed in isolation, making it easier to detect and measure changes without the complexity of managing live, interconnected pipeline templates across the entire system.
3Extent of automation
If pipeline templates are compiled and executed for each software release on cloud platforms, then automated deployment is achieved, but the compilation process becomes computation-intensive and time-consuming when managing hundreds of thousands of pipelines in multi-tenant systems
Solution Approach 1:
The patent merges the compilation of pipeline templates into reusable deployment packages that can be shared across multiple services and data centers. Instead of compiling the same pipeline logic repeatedly for each deployment target, the system compiles once at the organization or data center level and reuses the resulting deployment packages. This merging dramatically reduces computational resources required while maintaining full automation, as the heavy lifting is performed once and then distributed efficiently.
Solution Approach 2:
The patent implements dynamic pipeline template compilation where the system determines whether full recompilation is necessary or if existing deployment packages can be reused. Version pointers and dependency tracking enable the system to dynamically assess what needs to be recompiled versus what can be cached and reused. This dynamic approach optimizes computational resource usage by avoiding redundant compilations while still maintaining up-to-date pipelines when changes occur.
Data Source
AI summary
Computing systems, for example, multi-tenant systems deploy software artifacts in data centers created in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system receives pipeline templates including templating expressions that can be substituted with values for generating pipelines. A pipeline can be executed to perform a set of actions associated with continuous delivery of a software artifact. The system stores sets of partially hydrated pipeline templates. The partially hydrated pipeline templates can be compiled into executable pipelines associated with services configured on a datacenter of a cloud platform. The system stores different versions of pipeline templates as deployment packages. The system stores version pointers that identify specific deployment packages that are selected when a software release is deployed. The version pointers allow the deployment package to be updated in case of roll back or for deploying more recent changes.