Edge Terminal Job Scheduling via AI Model Activation
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Solution Overview
Problem
Existing IoT edge terminals are limited in their ability to perform jobs dynamically based on user-specified conditions, and they continue to execute pre-set jobs even when resources are scarce.
Innovation Solution
A method and system that determine a job and an edge terminal capable of performing that job under conditions specified by a user, utilizing an artificial intelligence-based model and dynamic group management to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If edge terminals always perform pre-set jobs, then job execution reliability is improved, but resource waste increases when resources are limited
Solution Approach 1:
The patent implements dynamic job execution by allowing edge terminals to adjust job performance based on real-time resource conditions. The system transitions from static pre-set job execution to dynamic decision-making where jobs are executed only when resources are sufficient, resolving the contradiction between reliable execution and resource waste.
Solution Approach 2:
The patent employs feedback mechanisms where the server monitors resource usage rates of edge terminals and adjusts job assignment accordingly. This closed-loop control ensures that jobs are assigned only when terminals have sufficient resources, preventing resource exhaustion while maintaining execution reliability.
2Productivity
If edge terminals execute jobs without condition checking, then productivity is improved, but system adaptability to user conditions deteriorates
Solution Approach 1:
The patent applies preliminary action by having the server pre-evaluate whether edge terminals meet user-specified conditions before assigning jobs. This advance checking ensures that only suitable terminals receive jobs, maintaining both execution speed and adaptability to user requirements.
Solution Approach 2:
The server acts as an intermediary between users and edge terminals, translating user conditions into executable job assignments. This mediator role ensures that user-specified conditions are properly evaluated and enforced without compromising execution efficiency.
3Device complexity
If the server assigns jobs to any edge terminal, then device complexity is reduced, but job completion reliability deteriorates when resources are insufficient
Solution Approach 1:
The server monitors resource usage rates and uses this feedback to make intelligent job assignment decisions. This simple yet effective feedback mechanism ensures that jobs are assigned only to terminals with sufficient resources, maintaining high completion reliability without complex terminal-side logic.
Solution Approach 2:
Edge terminals self-report their resource usage rates to the server, enabling the server to make informed assignment decisions. This self-service approach maintains simplicity while improving reliability, as terminals provide the necessary information without requiring complex decision-making capabilities.
Data Source
AI summary
A method for performing a job, performed by a computing device is provided. The method may comprise determining a first job that is performed under conditions specified by a user, determining an edge terminal of performing the first job and activating an artificial intelligence-based model required for the edge terminal to perform the first job, wherein the conditions include at least one of time zone information, regional information, and event information.