Edge Data Prioritization Using Content-Relevance Models
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Solution Overview
Problem
Existing edge network environments face challenges in efficiently prioritizing data transmission based on content relevance rather than source or deadline, leading to suboptimal latency and resource utilization in edge computing scenarios.
Innovation Solution
Implementing adaptive packet prioritization at the edge devices layer using global observability and data relevance models to analyze data streams, determining priority based on content significance and deadlines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data prioritization is based on source or deadline, then transmission scheduling is simplified, but data transmission efficiency and latency optimization deteriorate
Solution Approach 1:
The patent changes the prioritization parameter from static attributes (source ID, deadline) to dynamic content-based attributes. By analyzing data stream parameters and executing data relevance models, the system determines priority based on content significance, transforming how priority is calculated and assigned to data packets.
Solution Approach 2:
The patent introduces data relevance models as intermediaries between data streams and prioritization decisions. These models analyze data content and parameters to determine relevance scores, which then inform priority assignments. This intermediary layer enables sophisticated content-based prioritization without requiring complex direct analysis of all data attributes.
2Productivity
If comprehensive data analysis is performed to determine priority based on content relevance, then data transmission efficiency improves, but computational complexity and resource consumption increase
Solution Approach 1:
The patent extracts only the necessary data stream parameters needed for relevance determination rather than analyzing complete data content. By selecting specific parameters that indicate data significance, the system achieves content-based prioritization with reduced computational overhead compared to full data analysis.
Solution Approach 2:
The patent performs partial analysis of data content by focusing on key parameters and using data relevance models that process only essential information. This partial action approach achieves sufficient prioritization accuracy without the excessive computational cost of complete data inspection.
3Loss of time
If edge devices perform global observability and data relevance analysis, then latency is reduced through better prioritization, but resource utilization at edge devices increases
Solution Approach 1:
The patent performs preliminary analysis of data stream parameters and executes data relevance models in advance of actual data transmission decisions. By pre-determining priority levels based on content relevance, the system reduces latency during actual transmission without requiring intensive real-time resource consumption.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for edge data prioritization. An example apparatus includes at least one memory, instructions, and processor circuitry to at least one of execute or instantiate the instructions to identify an association of a data packet with a data stream based on one or more data stream parameters included in the data packet corresponding to the data stream, the data packet associated with a first priority, execute a model based on the one or more data stream parameters to generate a model output, determine a second priority of at least one of the data packet or the data stream based on the model output, the model output indicative of an adjustment of the first priority to the second priority, and cause transmission of at least one of the data packet or the data stream based on the second priority.