Multi-factor prediction for 2D to 3D modeling resource scheduling
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Solution Overview
Problem
Managing digital models in computing environments configured to virtually represent physical infrastructure, such as digital twins and metaverse applications, is challenging due to the varying computing resources required by different NeRF variant algorithms for 2D-to-3D modeling tasks.
Innovation Solution
A multi-factor prediction method is employed to estimate computing resources needed for algorithm execution, considering input, process, and output factors, and classifying them into near-linear and non-linear types to accurately schedule computing resources for efficient task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple NeRF variant algorithms are used for 2D-to-3D modeling tasks, then modeling accuracy and versatility are improved, but computing resource requirements and scheduling complexity increase
Solution Approach 1:
The patent changes the parameters of the scheduling system by introducing a multi-factor prediction model that evaluates algorithms based on multiple dimensions (computing power, memory, time, energy). This allows the system to adapt to different NeRF variant algorithms by adjusting prediction weights and thresholds rather than redesigning the entire scheduling architecture, thus maintaining versatility while controlling complexity.
Solution Approach 2:
The system performs preliminary prediction of computing resource requirements before actual algorithm execution. By pre-evaluating multiple factors (computing power, memory, time, energy) and classifying algorithms into categories, the scheduling complexity is reduced in advance, allowing for more efficient runtime decision-making when selecting among multiple NeRF variants.
2Productivity
If computing resources are allocated based on simple estimation, then scheduling speed is improved, but resource allocation accuracy deteriorates
Solution Approach 1:
The patent segments the computing resource estimation into multiple independent factors (computing power, memory, time, energy) rather than using a single aggregate estimate. Each factor can be calculated and predicted separately using appropriate models, then combined to form a comprehensive resource allocation decision. This segmentation allows for both speed (through modular calculation) and precision (through multi-dimensional evaluation).
Solution Approach 2:
The system introduces an intermediary prediction layer that mediates between simple estimation and complex exact calculation. The multi-factor prediction model acts as an intermediary that provides sufficiently accurate estimates without requiring exhaustive computation, balancing scheduling speed with resource allocation precision by predicting key parameters before full algorithm execution.
3Measurement precision
If comprehensive multi-factor prediction is performed, then computing resource allocation accuracy is improved, but prediction computation time increases
Solution Approach 1:
The patent applies partial action by selecting and weighting only the most critical factors for each specific scheduling scenario rather than always computing all possible factors at full depth. The system can adjust the level of prediction detail based on urgency and resource availability, performing comprehensive prediction when time permits and simplified prediction when rapid scheduling is needed, thus balancing accuracy with computation time.
Data Source
AI summary
Techniques are disclosed for multi-factor prediction of computing resources for algorithm execution. For example, a method comprises obtaining a set of factors associated with an algorithm configured to transform one or more two-dimensional images into one or more three-dimensional models. The method further comprises computing an estimated computing power value based on the set of factors. The method then comprises scheduling execution of the algorithm on one or more computing resources based on the estimated computing power value.