Method for reducing task unloading delay of 6G digital twin edge computing network

A technology of edge computing and edge server, applied in the direction of network traffic/resource management, electrical components, wireless communication, etc.

Active Publication Date: 2020-12-22
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to use digital twins (DTs) to estimate the state of the edge server and provide a dynamic mobile offloading scheme based on deep reinforcement learning (DRL) for mobile edge computing (MEC) in view of the deficiencies in the above-mentioned prior art , to achieve the purpose of minimizing the offloading delay at the cost of accumulated service migration during user movement, and give a training framework in Digital Twin Edge Computing Network (DITEN)

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  • Method for reducing task unloading delay of 6G digital twin edge computing network
  • Method for reducing task unloading delay of 6G digital twin edge computing network
  • Method for reducing task unloading delay of 6G digital twin edge computing network

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Embodiment Construction

[0083] Concrete implementation steps of the present invention are as follows:

[0084] Step 1, establish a digital twin edge computing model in 6G

[0085] Such as figure 1 Shown is the architecture of the Digital Twin Edge Computing Network (DITEN) in the future 6G wireless cellular network scenario, which consists of a physical physical layer and a DT layer. At the physical physical layer, edge servers are deployed on base stations (BSs) as small-scale computing units to provide computing services for mobile devices (MDs). The edge servers in the scene are represented as collections MDs, connected to edge servers through wireless communication within the coverage of BSs, offload their computing tasks to edge servers with specific needs during their movement. At the DT layer, the DTs of objects in MEC constitute the basic functions of the physical entity layer to help the entire service process achieve more efficient decision-making. The present invention considers two t...

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Abstract

The invention discloses a method for reducing task unloading delay of a 6G digital twin edge computing network. The method mainly provides a new digital twin edge computing network, uses digital twinto estimate the state of an edge server so as to provide training data, formalizes a series of unloading decision optimization problems in a user moving process, uses a Lyapunov optimization method tosimplify a long-term migration cost constraint into a multi-target dynamic optimization problem, and finally, a deep learning framework based on Actor-Critic is used for solving the calculation unloading optimization problem, and the training Agent is realized by a digital twin edge calculation network. According to the method, the unloading delay, the task failure rate and the migration rate arereduced to the maximum extent on the premise of keeping relatively low system cost.

Description

technical field [0001] The invention relates to the field of mobile edge computing, in particular to a method for reducing task offloading delay of 6G digital twin edge computing network. Background technique [0002] The sixth generation of telecommunication cellular networks (6G) is committed to providing better performance than 5G by digitizing the real network to enable wireless communication and computing. Mobile edge computing (MEC) is an important technology to realize mobile load in 6G. With the popularization and use of computing-intensive and time-sensitive applications, a large number of mobile IoT devices offload computing tasks to edge servers, causing problems such as network delay growth and user task offloading failures. Computing and storage servers will increase configuration costs. Therefore, it is particularly important to optimize the mobile edge computing offloading strategy. [0003] Existing inventions for the offloading problem of mobile edge comp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W28/08H04L29/08
CPCH04L67/10H04L67/1008H04W28/08
Inventor 张海宾孙文王榕张文琦张彦
Owner NORTHWESTERN POLYTECHNICAL UNIV
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