Unified Quota Mapping for Heterogeneous AI Accelerators
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Solution Overview
Problem
Conventional systems struggle with managing quotas for heterogeneous resources, particularly in cloud environments, as they fail to adapt to multiple resource dimensions and user requests, leading to complex and inefficient allocation of accelerators like GPUs and FPGAs.
Innovation Solution
A computer-implemented method utilizing machine learning models, such as decision trees and neural networks, to classify and map resource management sections to integer values, enabling efficient quota deduction and configuration of accelerators for AI workloads by simplifying quota management across different accelerators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional quota management systems are used for heterogeneous resources, then system simplicity is maintained, but adaptability to multiple resource dimensions and user requests deteriorates
Solution Approach 1:
The patent transforms heterogeneous resource specifications into unified integer parameters through machine learning models. The system extracts resource management sections, classifies them using a first ML model, maps classifications to integer values using a second ML model, and deducts these integers from a quota management tree. This parameter transformation enables the system to handle diverse accelerator types (GPUs, FPGAs) and resource dimensions (memory, cores) through a standardized integer-based quota mechanism, resolving the contradiction between adaptability and complexity.
2Productivity
If manual quota management methods are used, then implementation simplicity is maintained, but productivity and efficiency of accelerator allocation deteriorates
Solution Approach 1:
The patent implements self-service automation through machine learning models that automatically classify resource requests and allocate quotas without manual intervention. The system extracts resource management sections from user requests, classifies them using trained ML models, maps classifications to integer values, and performs automatic quota deduction from the quota management tree. This automated self-service mechanism significantly improves allocator efficiency and productivity while handling the complexity of heterogeneous resource management.
3Ease of operation
If heterogeneous accelerator resources are managed separately, then resource specificity is maintained, but ease of operation and management deteriorates
Solution Approach 1:
The patent creates a universal quota management system that handles multiple heterogeneous accelerator types (GPUs, FPGAs) and resource dimensions (memory, cores) through a single unified interface. The machine learning models translate diverse resource specifications into a common integer-based quota representation, allowing the quota management tree to uniformly manage all accelerator types. This universal approach simplifies operation and management while preserving the specific characteristics of different accelerator resources through the ML classification and mapping processes.
Data Source
AI summary
Embodiments receive a user request with resource specifications from an external application, extract select resource management sections from the received user request, classifying, the extracted select resource management sections using a first machine learning (ML) model which is trained using a historical dataset, map the classified extracted select resource management sections to at least one integer value, deduct the at least one integer value from a quota management tree to determine configuration specifications, and execute accelerators using the configuration specifications from artificial intelligence (AI) workloads.