Cloud Resource Allocation via Hardware Vector Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud platform resource allocation methods require extensive manual configuration and repeated efforts for each deployment, leading to inefficiencies and increased time and effort for engineers.
Innovation Solution
A method and device for resource allocation in a cloud platform that involves collecting initial hardware parameters, vectorizing them using AI models, calculating similarities with reference vector data, determining a deployment role, and adjusting similarity weights to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration and setup processes are used for deploying new systems or application services, then deployment control and customization are maintained, but time consumption and effort investment increase significantly
Solution Approach 1:
The patent pre-collects hardware parameters of electronic devices and pre-generates vector data representations during device provisioning. This preliminary action enables the deployment system to perform rapid similarity matching against deployment templates without requiring manual configuration during actual deployment, thus resolving the contradiction between deployment efficiency and time consumption.
Solution Approach 2:
The system enables automated self-service deployment by allowing deployment templates to automatically match with electronic devices based on vector similarity of hardware parameters. The deployment process autonomously identifies suitable devices and allocates resources without human intervention, significantly improving productivity while reducing time and effort investment.
2Productivity
If automated deployment methods are implemented, then deployment efficiency improves, but system complexity and configuration requirements increase
Solution Approach 1:
The patent transforms complex hardware parameter comparisons into simplified vector similarity calculations. By converting hardware parameters into vector representations and using similarity metrics, the system maintains high automation efficiency while reducing the perceived complexity of the deployment process, as the matching logic becomes more intuitive and manageable.
Solution Approach 2:
The patent introduces vector data as an intermediary representation between hardware parameters and deployment decisions. This intermediary layer simplifies the complexity by providing a standardized, computationally efficient format for comparing devices against deployment templates, making the automated system easier to manage and configure.
3Measurement precision
If hardware parameters are precisely matched with deployment roles, then resource allocation accuracy improves, but calculation complexity and processing time increase
Solution Approach 1:
The patent maintains high matching accuracy by transforming hardware parameters into vector representations that preserve the essential characteristics needed for accurate matching. The vector similarity calculation efficiently computes matching accuracy without requiring complex bitwise comparisons or detailed parameter-by-parameter analysis, thus resolving the contradiction between precision and processing time.
Data Source
AI summary
A method for resource allocation of a cloud platform is provided. The method comprises: collecting an initial hardware parameter of an electronic device based on the electronic device accessing the cloud platform, vectorizing the initial hardware parameter to obtain a base vector data, traversing a vector database to calculate similarities between the base vector data and each reference vector data in the vector database sequentially, determining a target reference vector data based on a maximum similarity, generating a deployment role based on the target reference vector data, invoking and running a deployment program corresponding to the deployment role. A resource allocation efficiency of a cloud platform can be improved and a resource allocation cost can be reduced.