Prioritization Model for Computing Resource Allocation
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Solution Overview
Problem
Content providers face inefficiencies in using computing resources to respond to content requests, as many requests consume resources without resulting in positive outcomes, leading to allocation issues and reduced availability for high-priority requests.
Innovation Solution
A data processing apparatus that receives content requests, extracts features, and uses a prioritization model to determine priority values, throttling access to computing resources based on these values and a specified threshold, while also updating the model with holdout requests to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If computing resources are allocated to all content requests, then service coverage is maximized, but resource efficiency deteriorates due to many requests not yielding positive outcomes
Solution Approach 1:
The system performs preliminary classification of content requests using a trained prioritization model before allocating computing resources. By extracting features from incoming requests and predicting positive outcomes in advance, the system identifies high-priority requests that warrant resource allocation, thereby avoiding waste on low-priority requests while maintaining service coverage for valuable requests.
Solution Approach 2:
The system applies different resource allocation strategies to different types of requests based on their predicted value. High-priority requests receive full computing resource allocation, while low-priority requests are throttled or denied. This localized quality approach ensures resources are concentrated on requests most likely to yield positive outcomes rather than uniform allocation.
2Loss of energy
If a prioritization model is used to throttle access to computing resources, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The system uses a trained prioritization model that captures complex decision-making patterns in a reusable form. The model, trained on historical request data and outcomes, copies successful allocation strategies into a predictive framework that can be applied to new requests without requiring complex real-time analysis, thereby managing system complexity while maintaining efficiency.
Solution Approach 2:
The prioritization model automatically classifies and prioritizes requests based on extracted features and historical patterns, without requiring manual intervention or complex real-time decision-making logic. The system self-adjusts by continuously training on new data, reducing the need for manual system management and complexity.
3Loss of energy
If computing resources are restricted to high-priority requests only, then resource efficiency is maximized, but service quality deteriorates for low-priority requests
Solution Approach 1:
The system applies partial resource allocation to low-priority requests rather than complete denial. By providing limited computing resources to low-priority requests, the system maintains basic service quality and reliability for these requests while concentrating full resources on high-priority requests, achieving a balance between efficiency and service quality.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for efficiently using computing resources when responding to content requests. Methods include using a prioritization model and a specified threshold specifying the maximum allowable negative outcome for a content provider, to determine whether a received content request is a low priority request. Methods further include throttling access to computing resources to respond to low priority requests, while providing access to computing resources for other content requests. Methods also include regularly updating the prioritization model and the specified threshold based on data for a new set of content requests.