Cloud Workload Deployment Automation Pipeline
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Solution Overview
Problem
The manual and time-consuming process of deploying software workloads across cloud computing environments is inefficient, often resulting in errors and increased costs due to the need for manual identification and replacement of unique object identifiers across different environments.
Innovation Solution
An automation pipeline that matches object names between source and destination environments to determine corresponding unique identifiers, allowing for seamless deployment of workloads without manual intervention or complex database management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to deploy workloads across cloud environments, then flexibility and control are maintained, but deployment time and error rates increase significantly
Solution Approach 1:
The patent introduces an automation pipeline as an intermediary system that bridges source and destination cloud environments. This pipeline automatically manages identifier replacement and workload deployment, eliminating the need for manual intervention while maintaining control and accuracy. The pipeline serves as a mediator that handles the complex transformation processes between different cloud environments.
Solution Approach 2:
The patent replaces manual mechanical processes (engineers manually identifying and replacing identifiers) with an automated computational system. The automation pipeline uses software-based mechanisms to perform identifier matching and replacement, substituting human manual operations with automated digital processes that are faster and more accurate.
2Measurement precision
If manual identifier replacement is performed across multiple cloud environments, then accuracy can be verified, but the complexity and time consumption increase
Solution Approach 1:
The automation pipeline performs self-service by automatically identifying, matching, and replacing identifiers across cloud environments without requiring external manual verification. The system independently manages the entire identifier replacement process, reducing the need for human intervention while maintaining accuracy through automated validation mechanisms.
Solution Approach 2:
The patent changes the state of identifier management from static manual processes to dynamic automated processes. The automation pipeline dynamically adapts identifier replacements based on environment-specific parameters, automatically adjusting the deployment process to match destination environment requirements without increasing operational complexity.
3Reliability
If separate cloud environments are created for different teams and functions, then independence and security are improved, but the number of environments and management overhead increase
Solution Approach 1:
The automation pipeline provides a universal solution that works across multiple separate cloud environments, teams, and functions. It serves as a multi-functional system that can deploy workloads to any destination environment while maintaining the independence and security of each environment, reducing management overhead through a single automated platform.
4Ease of operation
If manual deployment processes are used, then detailed control over each step is maintained, but labor costs and human error increase
Solution Approach 1:
The automation pipeline acts as an intermediary that maintains detailed control over each deployment step while eliminating manual labor. It provides transparent, automated control mechanisms that ensure accuracy without requiring human operators to manually manage each step, thereby improving efficiency while preserving operational control.
Data Source
AI summary
Techniques described herein relate to modifying and deploying workloads across cloud computing environments. Workloads such as a contact flow executing in contact center computing environment may interact with various other objects and resources in the computing environment, such as queues, flows, prompts, etc. Contact flows may be developed and tested in source computing environments and then deployed to one or more production environments. In various examples described herein, an automation pipeline may determine a number of objects in a source computing environment referenced by the workload, determine the corresponding objects in the destination computing environment, and determine and replace the unique identifiers associated with the referenced objects to allow the workload to be deployed in the destination environment. Additional techniques described herein include user access verification within both source and destination environments to further automate the integration and deployment processes for the workload.