Cloud Cost Tracking for Deployable Instance Selection
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Solution Overview
Problem
Cloud computing environments face challenges in efficiently selecting the most cost-effective cloud provider that meets the resource requirements for deployable instances, as different cloud providers have varying billing schemes and capabilities, leading to suboptimal resource allocation and increased costs.
Innovation Solution
A server computer system that receives resource requirement profiles and cost information from multiple cloud providers, calculates deployment costs, and selects the lowest cost cloud that meets the requirements, either fully or partially, to launch deployable instances, ensuring cost-effective resource allocation while achieving optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If cloud provider selection is made without systematic cost analysis, then deployment speed is improved, but operational expenses increase
Solution Approach 1:
The system performs preliminary cost analysis and cloud provider evaluation before deployment by calculating deployment costs based on resource requirement profiles and provider billing schemes, storing these costs in a data structure, and determining the lowest cost cloud in advance, thus avoiding expensive re-evaluation later while maintaining fast deployment
Solution Approach 2:
The patent introduces an intermediary cost analysis system that sits between the deployment request and cloud provider selection, automatically evaluating multiple providers against resource requirements and billing schemes to identify the optimal choice, thereby eliminating the need for manual cost-benefit analysis while ensuring cost-effective decisions
2Loss of energy
If multiple cloud providers are evaluated with detailed cost analysis, then operational expenses are reduced, but deployment complexity increases
Solution Approach 1:
The system employs a universal cost analysis framework that handles multiple cloud providers with different billing schemes through a single standardized process, using a common data structure to store and compare costs across providers, thus reducing operational expenses without proportionally increasing system complexity
Solution Approach 2:
The patent transforms the complex problem of multi-provider evaluation into a parameter-based comparison system, where deployment costs are calculated as functions of resource requirements and billing scheme parameters, allowing systematic evaluation through parameter manipulation rather than complex structural changes
3Loss of energy
If cloud provider selection optimizes for lowest cost, then operational expenses are reduced, but resource allocation efficiency decreases
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor deployment costs and resource allocation outcomes, using this information to refine cost calculations and improve future provider selections, thereby simultaneously reducing operational expenses and enhancing resource allocation efficiency through iterative optimization
Data Source
AI summary
A method includes receiving a request to launch a deployable instance, a resource requirement profile for the deployable instance, and corresponding cost model information for each cloud of a plurality of clouds. The method further includes calculating a corresponding deployment cost for the deployable instance for each cloud of the plurality of clouds by using at least a portion of the resource requirement profile for the deployable instance and the corresponding cost model information. The method further includes ranking the plurality of clouds in view of the corresponding deployment cost for each cloud of the plurality of clouds to determine a priority ranking. Responsive to identifying a lowest cost cloud of the plurality of clouds in view of the priority ranking, the method further includes launching the deployable instance on the lowest cost cloud.