Automotive Edge Computing Task Scheduling

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Solution Overview

Problem

In the automotive industry, managing sophisticated in-vehicle systems with limited computational power and storage requires effective scheduling in edge or cloud server collaborations, otherwise leading to unbalanced workload distribution, longer latency, and unnecessary costs.

Innovation Solution

A system and method for managing an automotive edge computing environment that includes a processor and memory with modules for receiving status information, queuing computing tasks, selecting an optimization trigger number based on average task gaps, and generating updated data transfer and process schedules to optimize workload distribution and resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Power

If collaborative computing with edge or cloud servers is implemented, then computational power and storage are improved, but workload distribution becomes unbalanced and latency increases without proper scheduling

Engineering Contradiction:

Improvecomputational powerVSAvoidlatency

Core Design Contradiction:

PowerVSLoss of time

Solution Approach 1:

The scheduling approach dynamically adjusts task allocation based on real-time server status information, making the system adaptive to changing conditions rather than using static scheduling rules

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system receives current status information from edge servers and uses this feedback to continuously optimize scheduling decisions, creating a closed-loop control system that responds to actual system state

Inventive Principle:

Principle #23Feedback

2Power

If collaborative computing with edge or cloud servers is implemented, then computational power and storage are improved, but workload distribution becomes unbalanced without proper scheduling

Engineering Contradiction:

Improvecomputational powerVSAvoidworkload distribution balance

Core Design Contradiction:

PowerVSEase of operation

Solution Approach 1:

The scheduling system dynamically adapts to server status changes and adjusts workload分配 in real-time, transforming static imbalance into a dynamically balanced state

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary scheduling actions based on predicted server status and task requirements, preventing workload imbalance before it occurs rather than correcting it afterward

Inventive Principle:

Principle #10Preliminary action

3Manufacturing precision

If optimization process is performed on all queued computing tasks, then scheduling quality is improved, but processing time exceeds the average time gap between tasks

Engineering Contradiction:

Improvescheduling qualityVSAvoidoptimization processing time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs optimization on a selective subset of queued tasks rather than all tasks, applying partial optimization action that balances quality improvement with time constraints

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The optimization process is segmented into batches of N tasks processed at a time, dividing the overall optimization workload into manageable segments that can be completed within time gaps

Inventive Principle:

Principle #1Segmentation

4Adaptability or versatility

If in-vehicle systems support sophisticated automotive services, then service capability is improved, but computational power and storage limitations are exceeded

Engineering Contradiction:

Improveservice capabilityVSAvoidcomputational power

Core Design Contradiction:

Adaptability or versatilityVSPower

Solution Approach 1:

Edge servers act as intermediaries between in-vehicle systems and cloud infrastructure, providing computational power and storage resources to sophisticated services without requiring these resources to be physically present in the vehicle

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system transitions from a single-dimension local computing model to a multi-dimensional collaborative computing model that includes vehicle, edge, and cloud dimensions, effectively expanding available computational resources

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS20220116479A1Systems and methods for managing an automotive edge computing environment

Publication Date: 2022.04.14 TOYOTA JIDOSHA KK

AI summary

Systems and methods described herein relate to managing an automotive edge computing environment. One embodiment receives current status information from one or more edge servers; receives and queues requested computing tasks from one or more connected vehicles; selects, as an optimization trigger number N, a largest number of requested computing tasks for which an optimization process can be completed within a time, per requested computing task, that is less than an average time gap between the requested computing tasks; performs the optimization process when a number of queued requested computing tasks exceeds the optimization trigger number N, wherein the optimization process produces an updated data transfer schedule and an updated data process schedule for N queued requested computing tasks; and transmits the updated data transfer schedule and the updated data process schedule to the one or more edge servers and the one or more connected vehicles.