AI-Based Resource Allocation for Critical Application Modules
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Solution Overview
Problem
Resource allocation in computing environments, such as cloud, datacenters, and edge devices, is inefficient due to power and computational resource constraints, particularly during critical application scenarios where grid failures or high demand occur, leading to drained resources and potential disruptions in critical functionalities.
Innovation Solution
A processor utilizes an AI system to analyze and determine critical application modules based on contextual scenarios, such as usage patterns and heat generation data, to allocate required resources like power and computational resources, ensuring availability by prioritizing and reallocating resources from non-critical to critical modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational resources are allocated to all application modules equally, then resource fairness is maintained, but critical application modules cannot receive prioritized resources during power or computational resource shortages
Solution Approach 1:
The system applies different resource allocation strategies to different application modules based on their criticality. Critical application modules receive prioritized resource allocation while non-critical modules receive reduced or deferred resources, creating localized quality differences in resource distribution rather than uniform allocation across all modules.
Solution Approach 2:
The system dynamically changes resource allocation parameters based on power availability and application criticality. When power shortages are detected, the system adjusts resource allocation parameters to favor critical applications, transforming the resource distribution state from static/equal to dynamic/prioritized.
2Reliability
If resources are reallocated from non-critical to critical application modules, then reliability of critical functions is improved, but resource management complexity increases
Solution Approach 1:
The system performs preliminary classification of application modules into critical and non-critical categories before resource allocation decisions are needed. This advance categorization simplifies real-time resource allocation during power shortages, as the system only needs to reference pre-determined criticality levels rather than performing complex analysis during resource constraints.
Solution Approach 2:
The system introduces an intermediary classification mechanism that acts as a mediator between application modules and resource allocation. This intermediary layer determines criticality based on predefined criteria and facilitates simplified resource distribution decisions, reducing the complexity of direct resource management.
3Measurement precision
If AI systems are used to analyze and determine critical application modules, then resource allocation accuracy is improved, but computational overhead increases
Solution Approach 1:
The system applies AI analysis selectively rather than continuously - performing comprehensive AI-based criticality assessment only when necessary (e.g., during power transitions or resource allocation decisions) rather than constantly monitoring and re-evaluating all applications, thus reducing overall computational overhead while maintaining accuracy when needed.
Data Source
AI summary
A processor may analyze, using an AI system, an application, where the application includes one or more application modules. The processor may determine, using the AI system, that an application module is critical based on a contextual scenario. The AI system may be trained utilizing data regarding heat generation of hardware on which the application module is operating. The processor may identify, using the AI system, required resources of the hardware for the application module to function during the contextual scenario. The processor may allocate an availability of the required resources for the application module.