Hybrid Resource Allocation Engine for Incomplete Policy Data
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Solution Overview
Problem
Organizations face challenges in accurately and efficiently allocating computer resources, particularly when policy data is incomplete, due to transient teams and collaborative resource usage, leading to high costs and subjective human-based allocation methods.
Innovation Solution
A hybrid rule-based and model-based system that generates resource allocation policies, utilizes usage data, and executes allocation engines to predictively allocate computer resources, even when data is incomplete, by employing machine-learning models and feature matching techniques to categorize and allocate resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual human-based allocation methods are used, then flexibility in handling incomplete policy data is maintained, but allocation accuracy and efficiency deteriorate
Solution Approach 1:
The system employs machine learning models that automatically learn from available usage data and policy information, enabling the allocation system to serve itself by making intelligent decisions without requiring complete manual policy definitions. The model-based approach allows the system to autonomously handle incomplete data scenarios.
Solution Approach 2:
The patent replaces manual human-based allocation mechanisms with an automated system combining rule-based and model-based approaches. This substitution eliminates the need for subjective human judgment while maintaining adaptability through the machine learning model that learns from historical data and usage patterns.
2Adaptability or versatility
If subjective human-based allocation methods are used, then handling of complex transient team scenarios is maintained, but allocation accuracy deteriorates
Solution Approach 1:
The system incorporates feedback loops where the machine learning model continuously learns from actual resource usage data and allocation outcomes. This feedback mechanism enables the system to improve its accuracy over time by adjusting its predictions based on real-world performance data, thereby achieving precise allocation for complex transient team scenarios.
Solution Approach 2:
The system performs preliminary analysis of usage patterns and team configurations before making allocation decisions. By pre-processing and understanding the characteristics of transient teams and their resource usage behaviors, the system can make more accurate allocations even in complex scenarios without requiring subjective human intervention.
3Ease of manufacture
If traditional allocation systems are used, then simplicity of implementation is maintained, but capability to handle large-scale resource usage deteriorates
Solution Approach 1:
The patent segments the allocation system into two distinct components: a rule-based engine for handling straightforward allocation scenarios and a model-based engine for complex situations. This segmentation allows the system to maintain simplicity for common cases while gaining advanced capabilities for large-scale and complex resource usage through the machine learning component.
Solution Approach 2:
The hybrid system achieves multi-functionality by combining rule-based allocation (for simple, well-defined scenarios) with model-based allocation (for complex, large-scale scenarios). This universal approach allows a single system to handle both simple and complex allocation tasks effectively, maintaining ease of implementation for basic operations while providing advanced capabilities when needed.
Data Source
AI summary
A system and method for allocating computer resources. The method includes generating a resource allocation policy defined for an organization for allocating the computer resources, determining a resource category allocation based on the generated resource allocation policy, generating a usage data, allocating the computer resource based on at least one of the generated usage data or the determined resource category allocation, executing allocation engines to allocate remaining unallocated computer resources, and providing a predicted resource allocation to implement the allocating of the computer resource.