Mobile Edge Computing Offload for Latency Reduction

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Solution Overview

Problem

Current cloud-based computer architectures face latency issues that limit their effectiveness in time-restricted applications, particularly in dynamic and mobile environments like automated vehicles, where efficient computation offloading is needed to optimize computing resources.

Innovation Solution

The method involves a control unit in mobile end devices, such as vehicles, that dynamically offloads computational tasks to edge computers or cloud computers by assessing resource information and application requirements, allowing for real-time, efficient distribution of computing power within a network of vehicles, utilizing both stationary and mobile edge computers to optimize resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Power

If cloud-based computer architectures are used for computation offloading, then computing power can be provided, but latency increases and time restrictions cannot be met

Engineering Contradiction:

Improvecomputing powerVSAvoidlatency

Core Design Contradiction:

PowerVSLoss of time

Solution Approach 1:

The patent segments the centralized cloud computing architecture into distributed edge computing nodes positioned throughout the network. This segmentation allows computation tasks to be offloaded to nearby edge servers rather than distant centralized clouds, reducing signal path length and latency while maintaining computing power availability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces edge servers as intermediary computing resources between mobile end devices and centralized cloud computers. These edge servers act as mediators that provide computing power locally, eliminating the need for long-distance communication with centralized clouds and thus reducing latency for time-restricted applications.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of time

If edge computing is used to reduce latency, then response time improves, but the dynamic characteristics of mobile vehicles are not yet considered

Engineering Contradiction:

Improveresponse timeVSAvoidadaptability to mobile dynamics

Core Design Contradiction:

Loss of timeVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic task assignment that adapts to the mobility characteristics of vehicles. The system continuously monitors vehicle movement status and dynamically adjusts computation task allocation between edge servers and cloud computers, ensuring that time-restricted tasks are assigned to nearby edge servers while less time-sensitive tasks can use centralized cloud resources.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the parameter of computing resource selection based on vehicle mobility parameters. The system evaluates vehicle speed, location, and movement patterns to dynamically adjust which computing resources (edge or cloud) are assigned to specific tasks, optimizing both response time and adaptability to mobile dynamics.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If decentralized computing is implemented, then computing capacity is distributed, but appropriate assignment of computing tasks to computing resources becomes complex

Engineering Contradiction:

Improvecomputing capacity distributionVSAvoidtask assignment complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent uses parameter-based task assignment where tasks are categorized by time sensitivity and computing resource availability. The system changes assignment parameters dynamically based on vehicle mobility status, network conditions, and edge server availability, simplifying the complex task assignment problem into parameter-driven decision rules.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent creates a stable assignment framework that operates independently of complex real-time variations. By establishing predefined assignment rules and parameters that remain stable despite changing conditions, the system simplifies task assignment complexity while maintaining effective decentralized computing capacity distribution.

Inventive Principle:

Principle #39Inert atmosphere (Inert environment)

4Use of energy by moving object

If computation tasks are offloaded to external resources, then local computing load is reduced, but network dependency increases

Engineering Contradiction:

Improvelocal computing loadVSAvoidnetwork dependency

Core Design Contradiction:

Use of energy by moving objectVSReliability

Solution Approach 1:

The patent implements local quality by deploying edge computing servers in close proximity to mobile end devices. This allows computation tasks to be offloaded to local edge servers rather than distant centralized clouds, reducing network dependency and improving reliability while still reducing local computing load through effective task offloading.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11861407B2Method for managing computing capacities in a network with mobile participants

Publication Date: 2024.01.02 VOLKSWAGEN AG

AI summary

Technologies and techniques for a mobile end device to offload computing from the mobile end device to at least one edge computer and/or at least one cloud computer. Resource information may be obtained from the at least one edge computer and/or at least one cloud computer. Application information may be obtained from at least one system application in the mobile end device, and A computing capacity may be assigned for the at least one system application in the mobile end device to the at least one edge computer and/or the at least one cloud computer.