Cloud Automation Tool Cost-Aware Resource Allocation
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Solution Overview
Problem
Current cloud automation tools do not enable cost-aware resource allocation for cloud deployments, which is crucial for customers looking to optimize resource usage and reduce costs.
Innovation Solution
A cloud automation tool that deploys cloud templates to cloud zones based on a cost-aware placement policy, selecting resources with lower hourly rates to allocate resources efficiently across availability zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If cloud automation tools allocate resources without cost awareness, then resource allocation is simple and fast, but operational expenses increase and resource optimization is reduced
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing cost information for multiple cloud zones before deployment. The cloud automation tool retrieves this pre-computed cost data during resource allocation, avoiding real-time cost calculations and enabling cost-aware decisions without adding significant complexity to the deployment process.
Solution Approach 2:
The patent introduces an intermediary cost information layer between the deployment template and cloud zone selection. This intermediary provides cost metadata that guides the placement policy, allowing the system to make cost-optimized decisions without requiring complex real-time analysis or direct integration with cloud provider pricing APIs during deployment.
2Productivity
If cloud automation tools use cost-aware placement policy, then resource allocation efficiency improves and costs are reduced, but the complexity of the deployment system increases
Solution Approach 1:
The system changes the parameter set used for cloud zone selection by incorporating cost information as an additional sorting criterion. The placement policy evaluates multiple parameters (cost, availability, performance) and sorts cloud zones accordingly, enabling efficient cost-aware allocation while maintaining a systematic approach that doesn't overly complicate the deployment workflow.
Solution Approach 2:
The cloud automation tool automatically retrieves and processes cost information without requiring manual intervention or complex configuration from users. The system self-services by autonomously evaluating cost data and selecting optimal cloud zones based on the placement policy, improving efficiency while keeping the user interface simple.
3Loss of energy
If cloud zones are sorted and selected based on cost, then the most economical options are chosen, but additional processing time is required for cost evaluation
Solution Approach 1:
Cost information for cloud zones is pre-computed and stored before the deployment process begins. This preliminary action eliminates the need for real-time cost calculations during resource allocation, allowing the system to quickly retrieve and compare pre-existing cost data without adding significant processing time to the deployment workflow.
Data Source
AI summary
An example system obtains a request to deploy a cloud template, sorts a plurality of cloud zones in a list based on cost, the plurality of cloud zones being candidates to execute the cloud template, selects a first one of the cloud zones in the list based on the first one of the cloud zones corresponding to a first cost that is lower than second costs of other ones of the cloud zones in the list, and deploys the cloud template on one or more instances of the computing resources corresponding to the first one of the cloud zones.