Request Distribution System for Game Resource Allocation
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Solution Overview
Problem
Video games require significant computing resources to maintain smooth gameplay, especially in simulations that demand complex and timely calculations, posing challenges in efficiently distributing and processing tasks such as character animations, music, and object textures within strict time constraints.
Innovation Solution
A request distribution system utilizing machine learning models to select and allocate tasks to a subset of workers, each generating different outputs based on defined quality levels, ensuring timely completion within specified time limits, and transmitting the highest quality output to the game application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple workers are used to generate different quality outputs for the same task, then output quality and selection flexibility improve, but system complexity and resource management difficulty increase
Solution Approach 1:
The system segments the worker pool into multiple independent workers, each capable of generating outputs at different quality levels for the same task. This segmentation allows the system to select from multiple quality options without increasing individual worker complexity, as each worker operates independently with its own quality characteristics.
Solution Approach 2:
The system changes the quality parameter of outputs by selecting from different workers rather than modifying individual worker behavior. Each worker is configured with specific quality characteristics, and the system manages complexity by selecting appropriate workers based on quality requirements rather than attempting to dynamically adjust worker parameters.
2Measurement precision
If machine learning models are used to select workers based on historical data, then task execution accuracy and quality improvement, but computational overhead and processing time increase
Solution Approach 1:
The machine learning models perform worker selection in advance by analyzing historical task execution data and identifying optimal workers before actual task assignment. This preliminary analysis enables the system to pre-determine which workers are most likely to produce high-quality outputs, reducing decision-making time during actual task execution.
Solution Approach 2:
The system implements feedback loops where historical task execution results are continuously fed back into the machine learning models. This feedback mechanism improves worker selection accuracy over time by learning from past performance patterns, while the models are optimized to provide predictions within acceptable time constraints for real-time task assignment.
Data Source
AI summary
Embodiments of the systems and methods disclosed herein provide a request distribution system in which a request for resources may be executed by a plurality of workers. Upon receiving a request for resources from a user computing system, the request distribution system may select a subset of workers from the plurality of workers to execute the request within a time limit. Once the workers generate a plurality of outputs, each output associated with a quality level, the request distribution system may transmit the output associated with the highest quality level to the user computing system.