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MEC random task migration method based on machine learning

A technology of machine learning and learning algorithms, applied in the field of mobile networks

Active Publication Date: 2019-05-14
BEIJING UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The method proposed by the invention solves the problem of generating the optimal migration strategy when the device-related and device-independent tasks arrive at random, and can realize online learning at the same time

Method used

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  • MEC random task migration method based on machine learning
  • MEC random task migration method based on machine learning
  • MEC random task migration method based on machine learning

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Experimental program
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Embodiment Construction

[0024] The invention considers the task scheduling problem in the random multi-task scenario of the mobile device in the single-user MEC system, and solves the problem of generating the optimal scheduling strategy when the device-related and device-independent random tasks arrive.

[0025] The single-user MEC system structure is as follows: figure 1 As shown, it mainly includes a mobile device and a MEC server. The mobile device is mainly composed of a task cache queue, a policy generator, a transmission unit, and a processing unit; the MEC server mainly uses its processor unit. Here, it is assumed that the processor The computing power is sufficient, and there is no task queuing.

[0026] The overall training process of the method proposed by the present invention is as follows: when a random task arrives, it is temporarily stored in the cache queue, and when the system schedules the task to be executed, the task is divided into two non-migratable device-related components an...

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Abstract

The invention discloses a random task migration method based on machine learning. A single task is divided into N migratable components irrelevant to equipment and two non-migratable components relevant to the equipment. A Markov decision process is used for modeling a system, a Q learning algorithm in reinforcement learning is used for generating an optimal migration strategy for determining taskcomponents, task component data and the optimal strategy are recorded to serve as training samples, so that the deep neural network is trained, and the learning capacity of the neural network is stronger along with continuous increase of the training samples. When the neural network accuracy reaches a certain degree, the approximate optimal migration strategy of the random task can be obtained only through one-time forward propagation. According to the method provided by the invention, the generation problem of the optimal migration strategy when the equipment-related and equipment-irrelevanttasks randomly arrive is well solved, and meanwhile, online learning can be realized.

Description

technical field [0001] The invention belongs to the technical field of mobile networks, and in particular relates to a machine learning-based MEC random task migration method. Background technique [0002] The rapid change of mobile applications has brought many new functions and new experiences to users, but it has brought greater challenges to the limited computing power and battery power of mobile devices. The proposal of mobile edge computing (Mobile-edge computing, MEC) has brought a solution to this problem: by deploying high-performance servers on the mobile access network side, some computing tasks can be migrated to adjacent MEC servers for execution, which can ease the problem of mobile computing. The ever-increasing demand for application computing puts pressure on the computing power and battery power of mobile devices, reducing the execution delay of mobile device applications and the energy consumption of mobile devices, and greatly improving user experience. ...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N99/00H04L29/08G06N3/08
CPCY02D10/00
Inventor 霍如孟浩刘江郭倩影谢人超黄韬刘韵洁
Owner BEIJING UNIV OF TECH
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