Automated Software Deployment via Data Association
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Solution Overview
Problem
The rapid frequency of software updates and deployments in modern software systems, often requiring coordination across multiple machines and geographical locations, leads to inefficiencies and errors due to the reliance on human intervention, which is costly and error-prone, especially when systems are sensitive and require defined deployment windows.
Innovation Solution
An automated deployment system that uses deployment logic, release data, and environmental data to generate deployment plans and objects, enabling the automatic deployment of software artifacts across target systems without user intervention, by determining associations between deployment steps and artifacts, and configuration information, thus streamlining the process and reducing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated deployment is implemented, then deployment time and human error are reduced, but system complexity increases due to the need for deployment logic, release data, and environmental data management
Solution Approach 1:
The deployment system is segmented into distinct data structures: deployment logic (defining deployment steps), release data (defining software artifacts), and environmental data (defining target systems). This segmentation allows each component to be independently managed and combined to create deployment plans, reducing overall system complexity while enabling automation.
Solution Approach 2:
The system uses an intermediary data processing layer that takes deployment logic, release data, and environmental data as inputs, processes them to generate deployment plans and objects, and executes the deployment. This intermediary layer abstracts the complexity of coordination between multiple machines and geographical locations, providing automated deployment without exposing the user to underlying system complexity.
2Device complexity
If manual deployment coordination is used, then system complexity is lower, but deployment time increases and human error increases
Solution Approach 1:
The deployment system performs self-service by automatically generating deployment plans and executing deployments without requiring manual coordination. The system uses deployment logic to automatically determine deployment steps, release data to identify software artifacts, and environmental data to configure target systems, thereby increasing deployment speed while maintaining manageable complexity through structured data organization.
3Productivity
If deployments are performed frequently to keep up with development cycles, then software update speed increases, but the risk of errors and coordination failures increases
Solution Approach 1:
The system incorporates feedback mechanisms where deployment plans are generated based on deployment logic, release data, and environmental data, and deployment objects are created and executed systematically. This structured feedback loop ensures that each deployment step is validated and coordinated correctly, maintaining high reliability even during frequent deployments.
Solution Approach 2:
The system performs preliminary actions by pre-defining deployment logic, release data, and environmental data before actual deployment occurs. Deployment plans are generated in advance based on these pre-configured elements, allowing for systematic execution and reducing the risk of errors during frequent updates.
Data Source
AI summary
Particular deployment logic is selected that describes a plurality of steps in a type of software deployment. Release data is identified that defines a selection of a set of software artifacts to be deployed in a particular deployment. Further, environmental data is selected that describes configuration of a target system for the particular deployment. First associations are determined, using data processing apparatus, between steps in the plurality of steps and software artifacts in the set of software artifacts. Second associations are determined between steps in the plurality of steps and configuration information of the target system used in the respective steps. The artifacts are automatically deployed on the target system, using one or more data processing apparatus, based on the first and second associations.