Automatic Task Scheduling for Edge Computing Privacy

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Solution Overview

Problem

Existing edge computing systems with GPUs require professional operation and are costly when privacy is enforced, lacking efficient automated task scheduling solutions.

Innovation Solution

An electronic device with a storage medium and processor implements an automatic task scheduling method, breaking down data processing tasks into job tasks, forming a job queue, and distributing computing resources based on task requirements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If edge computing apparatus with GPU is provided to ensure data privacy, then data privacy is protected, but operation complexity and cost increase requiring professional personnel

Engineering Contradiction:

Improvedata privacyVSAvoidoperation complexity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system implements automated task scheduling that enables the edge computing apparatus to autonomously manage and allocate computing resources without requiring professional personnel intervention. The scheduling system automatically breaks down tasks, forms job queues, and distributes resources based on task requirements, allowing the system to serve itself rather than requiring expert operation.

Inventive Principle:

Principle #25Self-service

2Reliability

If professional personnel operate the edge computing apparatus with GPU, then task execution reliability is improved, but operation cost increases

Engineering Contradiction:

Improvetask execution reliabilityVSAvoidoperation cost

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The automated scheduling system enables the edge computing apparatus to autonomously manage task execution, eliminating the need for professional personnel and reducing operational costs while maintaining reliable task completion through systematic resource allocation and monitoring.

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If manual task scheduling is used in edge computing system, then flexibility in resource allocation is improved, but productivity and efficiency deteriorate

Engineering Contradiction:

Improveresource allocation flexibilityVSAvoidtask scheduling efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system replaces manual mechanical scheduling operations with an automated electronic scheduling mechanism that automatically breaks down tasks, forms job queues, and distributes computing resources. This substitution maintains flexibility through adaptive resource allocation while dramatically improving productivity by eliminating manual intervention and enabling systematic automated management.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Productivity

If automated task scheduling is implemented, then productivity and efficiency are improved, but system complexity increases

Engineering Contradiction:

Improvetask scheduling efficiencyVSAvoidscheduling system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The scheduling system is divided into distinct functional modules: task breakdown module, job queue formation module, and resource distribution module. This segmentation organizes the complexity into manageable, independent components that can be developed, maintained, and understood separately, reducing the perceived complexity while maintaining high productivity through automated operation.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12236274B2Method for automatic scheduling of tasks, electronic device employing method, and computer readable storage medium

Publication Date: 2025.02.25 FULIAN PRESION ELECTRONICS (TIANJIN) CO LTD

AI summary

A method for the automatic scheduling of tasks obtains data processing tasks and data sources. A job queue is formed based on the data processing tasks. The job tasks are extracted in order from the job queue. Computing resources are distributed based on the extracted job tasks. A result of the data processing task is obtained by the pre-trained model based on the data source. An electronic device and a computer readable storage medium applying the method are also provided.