Multi-Cloud Cost Estimation via API Normalization
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Solution Overview
Problem
Cloud resource deployment costs are dynamic, opaque, and vary among providers, making it difficult for users to understand true costs pre-deployment, leading to inefficient and costly utilization due to lack of direct comparability across different cloud providers.
Innovation Solution
A multi-cloud cost estimation and recommendation system that predicts costs for different deployment mechanisms and providers by accessing current pricing through APIs, using resource mappings to normalize data and recommend optimal deployment environments based on resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If cloud resource deployment is performed without pre-deployment cost estimation, then deployment speed is improved, but deployment cost increases due to inability to compare costs across providers
Solution Approach 1:
The system performs cost estimation and provider comparison before actual resource deployment. By calculating predicted costs for multiple cloud providers in advance using their pricing APIs, the system enables informed decision-making that prevents costly post-deployment optimizations, thus reducing overall deployment cost without significantly impacting deployment time.
2Loss of energy
If detailed cost estimation and comparison across multiple providers is performed, then deployment cost is reduced, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary cost estimation system that acts as a mediator between users and multiple cloud providers. This system automatically queries pricing APIs, normalizes cost data from different providers, performs comparisons, and presents recommendations to users. By centralizing this complexity in an intermediary layer, individual users don't need to directly manage the complexity of multi-provider comparisons, thus reducing deployment cost without requiring users to handle system complexity.
3Adaptability or versatility
If resource requirements are converted to multiple provider-specific resource types, then adaptability across providers is improved, but data processing complexity increases
Solution Approach 1:
The system segments the resource specification process into distinct components: a provider-agnostic resource requirement definition phase and provider-specific resource mapping phase. By separating these concerns, the system maintains adaptability across multiple cloud providers while managing data processing complexity through structured, modular transformations rather than monolithic processing.
Data Source
AI summary
Data is received characterizing a virtual resource requirement for deployment of a resource in a first remote computing environment and/or a second remote computing environment. Second data is received characterizing resource cost for the first remote computing environment and the second remote computing environment. The receiving the second data includes accessing, via an application programming interface of the first remote computing environment and based on an account identity of an entity associated with the virtual resource requirement, the second data characterizing computing resource cost for the first remote computing environment. A first cost for deploying the resource within the first remote computing environment and a second cost for deploying the resource within the second remote computing environment is predicted using the received data. The first cost and the second cost is provided. Related apparatus, systems, techniques and articles are also described.