N-Dimensional Resource Vector Matching for Cloud Allocation
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Solution Overview
Problem
The complexity of selecting appropriate computing resources in cloud computing systems is exacerbated by the increasing variety of resources and diverse user needs, making traditional resource allocation methods inefficient.
Innovation Solution
The approach involves representing resources as n-dimensional vectors based on their properties, converting user requests into corresponding vectors, and calculating distances to identify the closest matching resources using a distance-based selection algorithm, which can incorporate weighting and threshold criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource allocation methods are used, then resource selection can be performed, but the complexity increases and efficiency decreases due to the increasing variety of resources and diverse user needs
Solution Approach 1:
The patent transforms resource properties and user requirements into parameterized vectors in an n-dimensional space. Each resource is represented by a vector of properties (e.g., computing power, storage, bandwidth), and each user requirement is represented by a vector of desired property values. This parameter transformation enables efficient mathematical operations for resource matching.
Solution Approach 2:
The patent introduces an n-dimensional coordinate system where each dimension represents a specific resource property. By mapping resources and requirements into this multi-dimensional space, the system can perform distance-based matching that simultaneously considers multiple properties, transforming a complex multi-criteria selection problem into a geometric distance calculation problem.
2Adaptability or versatility
If the number of computing resources with various capabilities increases, then resource versatility improves, but the difficulty of selecting appropriate resources increases
Solution Approach 1:
The patent creates a universal vector representation framework that can accommodate any type of computing resource (virtual machines, physical servers, storage devices, network resources) and any type of user requirement. The n-dimensional vector model serves as a universal language for representing diverse resources and requirements, enabling a single matching mechanism to handle all resource types.
Solution Approach 2:
The patent replaces complex manual or rule-based resource selection processes with automated vector-based distance calculation. Instead of manually evaluating multiple resource properties against user requirements, the system automatically computes Euclidean distances between resource vectors and requirement vectors, substituting mechanical evaluation with mathematical computation.
3Measurement precision
If detailed property values are considered for resource selection, then matching precision improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary normalization and standardization of resource property values before matching. All resource properties are pre-processed into a common scale and format, creating standardized vectors that can be directly compared. This preliminary action eliminates the need for complex real-time normalization during the matching process, reducing computational complexity while maintaining precision.
Data Source
AI summary
In one embodiment, an n-dimensional resource vector for each of a plurality of resources in a computer network is determined, each n-dimensional resource vector having n property values for a corresponding resource of the plurality of resources. Upon receiving a request for one or more resources of the plurality of resources, where the request indicates one or more desired property values, the techniques convert the desired property values of the request into an n-dimensional request vector, determine a distance between each resource vector and the request vector, and provide a response to the request, the response indicating one or more closest match resources for the request based on the distances.