Resource Partitioning for Supply-Constrained Demand Feasibility
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Solution Overview
Problem
Modern computing systems face challenges in managing supply-constrained resources, such as accelerator chips, due to unpredictable availability, making it difficult for users to assess feasibility of resource capacity under varying conditions.
Innovation Solution
A demand feasibility modeling system that utilizes a solver engine and capacity data calculator to determine partitioning of supply-constrained resources based on availability information, simulating supply and demand to provide answers on resource allocation feasibility, including potential parameter changes or task preemption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If supply constrained resources are pooled for shared use by multiple users with queuing and scheduling, then resource utilization is maximized, but availability becomes unpredictable
Solution Approach 1:
The system performs preliminary actions by calculating and providing feasibility assessments before users commit to resource usage. The solver engine evaluates whether requested resources can be fulfilled based on current and historical data, allowing users to make informed decisions about resource allocation before actual consumption occurs.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring resource availability, user requests, and feasibility outcomes. This feedback loop allows the system to update its models with actual performance data, improving future feasibility predictions and helping users understand real-time resource availability patterns.
2Reliability
If users are provided with detailed feasibility information before purchasing capacity, then user confidence increases, but system complexity increases
Solution Approach 1:
The solver engine acts as an intermediary between the complex resource allocation system and the user. It translates complex availability data, partitioning information, and scheduling constraints into simplified feasibility assessments that users can easily interpret, maintaining high user confidence without exposing them to system complexity.
Solution Approach 2:
The system creates simplified models and representations of the complex resource allocation problem. The solver engine generates feasibility predictions based on modeled supply and demand scenarios, providing users with actionable information without requiring them to understand the underlying complexity of resource pooling and scheduling algorithms.
3Measurement precision
If the system models supply and demand using historical data and machine learning, then feasibility accuracy improves, but computational requirements increase
Solution Approach 1:
The system dynamically adjusts modeling parameters based on data availability and computational constraints. The machine learning models can adapt their complexity and the granularity of historical data analysis based on current system conditions, maintaining high accuracy when computational resources are available while reducing processing requirements during constrained periods.
Data Source
AI summary
The present disclosure provides methods and systems for managing user queries concerning fulfillment of requests to use a supply constrained resource. A method may involve receiving a user query specifying a requested supply constrained resource, the user query including one or more parameters, providing the user query to a solver engine, accessing availability information indicating an availability of the supply constrained resource, determining a partitioning of the supply constrained resource based on the availability information, providing the determined partitioning of the supply constrained resource to the solver engine, determining, by the solver engine, a feasibility of a user request to use the supply constrained resource having the one or more parameters of the user query, and outputting, from the solver engine, the determined feasibility of the user request.