AI Platform Resource Allocation via Signaling Interaction
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Solution Overview
Problem
The existing AI platform intelligentialization process is inefficient due to redundant steps, manual intervention, and static resource allocation, leading to increased operating costs and reduced efficiency, particularly in model prediction and training processes.
Innovation Solution
The proposed solution involves improved AI resource allocation based on signaling transmission between the application side and the AI platform, enabling dynamic configuration control, reducing manual intervention, and optimizing resource allocation through configuration signaling interaction, which includes pre-satisfying application configurations for AI intelligentialization processes, eliminating redundant operations, and dynamically adjusting resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual resource allocation and model loading operations are performed for each prediction request, then resource allocation can be controlled, but system efficiency deteriorates due to redundant operations
Solution Approach 1:
The system performs model validity checks and resource allocations in advance before actual prediction requests arrive. The AI platform pre-loads valid models into memory and allocates computing resources (CPU, GPU) beforehand, so when prediction requests come in, these preliminary preparations are already complete, eliminating redundant operations for each request
Solution Approach 2:
Multiple prediction requests that require the same model are merged and processed together. The system identifies that multiple requests can share the same model and computing resources, so it combines them into a single processing operation, reducing the number of times the same model needs to be loaded and resources allocated
2Reliability
If model validity checks and resource allocations are performed for each prediction request, then resource management can be ensured, but processing time increases due to redundant operations
Solution Approach 1:
The system performs model validity checks and resource allocations in advance before actual prediction requests arrive. The AI platform pre-loads valid models into memory and allocates computing resources (CPU, GPU) beforehand, so when prediction requests come in, these preliminary preparations are already complete, eliminating redundant operations for each request
Solution Approach 2:
The system maintains models in memory and resources allocated continuously across multiple prediction requests. Instead of loading and allocating for each request separately, the model remains loaded in memory and resources stay allocated throughout the period of multiple requests, making the useful action continuous rather than repetitive
3Device complexity
If static resource allocation is used for AI intelligentialization processes, then resource allocation is simple to manage, but system adaptability deteriorates when workloads change
Solution Approach 1:
The system transitions from static to dynamic resource allocation. The resource allocation module continuously monitors the number of prediction requests and dynamically adjusts resource allocation accordingly. When more requests arrive, more resources are allocated; when fewer requests arrive, resources are released. This makes the system adaptable to changing workloads without complex manual reconfiguration
Data Source
AI summary
An electronic device (400) on an artificial intelligence (AI) platform side, the AI platform is capable of interacting with an application side to perform at least one AI intelligentialization process with the same application configuration, the electronic device (400) includes a processing circuitry (402) which is configured to: receive a configuration request for the at least one AI intelligentialization process from an application side, the configuration request including information indicating the application configuration; and when the application configuration is satisfied, assign the application configuration for usage by the at least one AI intelligentialization process during execution.