Cloud Automation Scaling via Dependency Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud computing platforms require significant manual effort and expertise to configure and manage resources, making it complex for users to take advantage of their capabilities effectively.
Innovation Solution
The implementation of a cloud automation system that automates management tasks such as provisioning virtual machines, scaling resources, and reclaiming unused resources, using a scalable architecture that allows for customizable cloud environments through interfaces and tools like the vCloud Automation Center and vRealize Automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user input and configuration is used to install, configure, and deploy cloud components, then users can control and customize their environment, but the complexity and time required for deployment increases significantly
Solution Approach 1:
The system enables self-service automation where the cloud automation system automatically provisions, configures, and manages cloud resources without requiring extensive manual user input. The automation engine executes workflows that automatically install and configure components based on predefined templates and configurations.
Solution Approach 2:
The system uses pre-defined blueprints, templates, and configurations that are prepared in advance. These preliminary configurations allow rapid deployment of cloud environments without manual step-by-step configuration, as the automation system executes pre-planned deployment sequences.
2Productivity
If manual configuration is used for cloud components, then users can ensure precise customization, but the deployment time and productivity are reduced
Solution Approach 1:
Deployment blueprints and configuration templates are prepared in advance with all necessary settings, dependencies, and sequences predefined. This allows the automation system to execute rapid deployments without time-consuming manual configuration steps during deployment.
Solution Approach 2:
The system replaces manual mechanical configuration operations with automated software-based workflow execution. The automation engine uses scripting and programming capabilities to perform configuration tasks automatically, significantly reducing deployment time compared to manual processes.
3Reliability
If cloud resources are scaled up to meet peak demand, then service availability is improved, but resource waste occurs during low demand periods
Solution Approach 1:
The system implements dynamic resource scaling where cloud resources can be automatically adjusted based on real-time demand conditions. The automation system monitors usage patterns and can provision additional resources during peak demand periods while releasing or reducing resources during low demand periods, optimizing both availability and resource utilization.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on demand conditions. Configuration parameters such as resource quantity, allocation ratios, and deployment scales are adjusted automatically to match actual usage patterns, allowing the system to maintain reliability during peak times while conserving resources during off-peak periods.
Data Source
AI summary
Methods and apparatus to scale in and/or scale out arbitrary resources managed by a cloud automation system are disclosed. An example apparatus includes processor circuitry; and a non-transitory computer readable medium comprising instructions which, when executed, cause the processor circuitry to: in response to an indication to scale a first component of an application to be deployed: determine an execution plan to scale the first component based on a dependency graph corresponding to a dependency within a blueprint specifying a logical topology of the application; perform a custom action to scale the first component, the custom action identified in a scaling parameter associated with the application; and update operation of a second component based on scaling the first component, the second component dependent on the first component, the update to enable the second component to interact with the first component after the scaling.