Resource Bundling via Discrete Choice Modeling
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Solution Overview
Problem
Resource provisioning platforms face challenges in optimally selecting and bundling resources across multiple categories to maximize performance due to exponential increases in potential bundles and inter-category dependencies, leading to inefficiencies and performance degradation.
Innovation Solution
A bundling system that collects historical transaction data, analyzes it using discrete choice modeling and marginal value estimation to determine substitution effects and joint dependencies across resources, and selects optimal bundles for provisioning, incorporating a substitution effect analyzer and joint dependence analyzer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system offers all possible resource combinations across multiple categories, then the completeness of resource options is improved, but the system complexity and computational burden increase exponentially
Solution Approach 1:
The patent segments the resource catalog into multiple independent categories (e.g., compute, storage, networking, security). By organizing resources hierarchically with category-level and resource-level identifiers, the system divides the exponentially large search space into manageable segments that can be processed independently, reducing computational complexity while maintaining completeness of options.
Solution Approach 2:
The patent introduces a new dimensional approach by creating a multi-dimensional resource space where resources are indexed not just by their properties but by their category memberships and compatibility relationships. This dimensional transformation allows the system to navigate the resource space efficiently using category-based filtering and constraint propagation, avoiding brute-force enumeration of all combinations.
2Measurement precision
If the system analyzes all resource bundles to optimize performance, then the accuracy of bundle selection is improved, but the processing time and computational resources increase exponentially
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing compatibility relationships, category hierarchies, and resource attributes in structured data formats. Historical transaction data is pre-processed to identify patterns and constraints. This preliminary preparation enables the optimization algorithm to work with pre-organized information rather than raw data, significantly reducing processing time during actual bundle selection while maintaining accuracy.
Solution Approach 2:
The patent implements partial action by analyzing only the subset of resource bundles that satisfy category constraints and compatibility rules, rather than evaluating all possible combinations. The system uses constraint propagation to eliminate infeasible bundles early in the process, focusing computational effort only on viable options, thus achieving accurate optimization without exponential processing time.
3Measurement precision
If the system accounts for intra-category and inter-category dependencies in bundling, then the accuracy of demand prediction is improved, but the computational complexity increases
Solution Approach 1:
The patent segments dependency analysis into intra-category dependencies (resources within the same category that compete or complement each other) and inter-category dependencies (relationships between different resource categories). This segmentation allows the system to model and analyze each type of dependency separately using appropriate mathematical models, reducing overall computational complexity while capturing the full scope of dependency effects on demand prediction accuracy.
Data Source
AI summary
A system, method and program product for bundling resources for a resource provisioning platform. A system is disclosed that includes a plurality of resources, wherein each resource belongs to one of a plurality of categories; a bundling system having: a data collection system that gathers historical transaction data associated with the resources; an analysis system that analyzes the historical transaction data to assign estimated valuations to different bundles of resources and includes (a) a substitution effect analyzer to analyze a substitution effect of resources in each category using discrete choice modeling and marginal value estimation, and (b) a joint dependence analyzer that determines intra-category and inter-category joint dependent inferences across all resources; and a bundle selection system that selects a set of bundles for provisioning based on the estimated valuations.