Native Storage Machine Learning for Resource Allocation
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Solution Overview
Problem
In environments where resource allocation requests between entities are not consistently met, leading to insufficient resource allocation, causing processing delays or halts in the first entity's operations.
Innovation Solution
A machine-learning processing system at native-location storage that receives and analyzes data sets for resource requests and communications to generate a resource-allocation specification, optimizing communication protocols and schedules for improved resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional resource allocation requests are transmitted without machine-learning optimization, then the communication process remains simple, but the resource allocation satisfaction rate is low and processing delays occur
Solution Approach 1:
The system performs preliminary actions by training machine learning models in advance using historical resource allocation data, communications data, and allocation outcomes. These pre-trained models are stored in the storage system and automatically applied to new resource allocation requests, enabling the system to predict optimal allocation strategies before actual resource requests are made, thereby improving satisfaction rates without adding real-time complexity
Solution Approach 2:
The storage system provides self-service capabilities by automatically executing machine learning inferences locally using stored models and data. The system autonomously determines optimal resource allocation strategies without requiring external intervention or complex real-time processing, improving reliability while maintaining operational simplicity through automated decision-making
2Productivity
If machine-learning models are trained and executed at native-location storage system, then resource allocation efficiency improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs model training in advance using historical data stored in the storage system, creating pre-trained machine learning models that are cached for future use. This preliminary training action eliminates the need for real-time model training, allowing the system to execute predictions quickly when resource allocation requests are made, thereby improving efficiency without incurring time penalties during critical operations
Solution Approach 2:
The system prepares computational resources and stores trained models in advance to cushion against future processing demands. By having pre-trained models readily available in the storage system, the system can quickly respond to resource allocation requests without experiencing time delays during actual operations, effectively buffering against productivity losses
Data Source
AI summary
Techniques are disclosed for using machine-learning processing for generating resource-allocation specifications. A first data set may be received from a first data source. The first data set can include a first resource request and a first timestamp associated with entities. A second data set can be received from a second data source that includes communication data and allocation data associated with the entities. Target characteristics may be defined for training instances. The training instances can be used to train a machine-learning model using the first data set and the second data set. A third data set may be accessed and used to generate a user session within which, the trained machine-learning model may execute to generate a resource-allocation specification. The resource-allocation specification including a communication schedule. One or more communications compliant with the communication schedule may be output to an entity.