Dynamic User Profile Projection for Edge Server Optimization
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Solution Overview
Problem
Conventional user profile projection systems inefficiently transmit and store user profiles at edge servers, wasting bandwidth and storage space by sending profiles regardless of future lookup requests, leading to unnecessary data transmission and storage.
Innovation Solution
A dynamic user profile projection system that predicts future lookup requests using historical data and a lookup prediction model, transmitting profiles only to edge servers with a predicted likelihood of future lookups, thereby minimizing unnecessary data transmission and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user profiles are transmitted to edge servers based on segmentation process, then user profile availability is improved, but bandwidth and storage space are wasted due to transmission of unwanted profiles
Solution Approach 1:
The system performs preliminary actions by predicting future lookup requests before they occur. The lookup prediction model analyzes historical data to forecast which user profiles will be requested, and the system proactively transmits only those predicted profiles to edge servers before actual requests arrive, avoiding transmission of unwanted profiles
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual lookup requests at edge servers and comparing them with predicted lookups. The lookup history and prediction accuracy are fed back to refine the prediction model, enabling the system to learn from past performance and improve future profile transmission decisions
2Speed
If user profiles are transmitted to all edge servers, then lookup speed is improved, but storage space at edge servers is wasted
Solution Approach 1:
The system applies local quality by customizing the user profile set at each edge server based on local prediction needs. Each edge server receives only the user profiles predicted to be relevant to its specific location and user base, rather than a universal set of all profiles, optimizing both storage efficiency and local lookup speed
3Device complexity
If conventional segmentation process is used for profile projection, then system simplicity is maintained, but operational efficiency deteriorates due to unnecessary data transmission
Solution Approach 1:
The system introduces an intermediary lookup prediction model that sits between the segmentation process and profile transmission. This intermediary component analyzes historical lookup data and predicts future requests, acting as a mediator that filters which profiles should be transmitted based on predicted demand, thereby improving operational efficiency without completely redesigning the segmentation process
Data Source
AI summary
Systems and methods for dynamic user profile projection are provided. One or more aspects of the systems and methods includes computing, by a prediction component, a predicted number of lookups for a future time period based on a lookup history of a user profile using a lookup prediction model; comparing, by the prediction component, the predicted number of lookups to a lookup threshold; and transmitting, by a projection component, the user profile to an edge server based on the comparison.