An MEC stochastic task migration method based on a Bayesian network

A technology of Bayesian network and Bayesian network, which is applied in the field of MEC random task migration based on Bayesian network, can solve the problems of loss of interaction ability and inconformity with practical applications, etc.

Inactive Publication Date: 2019-02-22
BEIJING UNIV OF TECH
View PDF3 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the MEC scenario, the user's mobile device will frequently interact with the operator's base station. If the overall migration solution is adopted, the interaction capability will be lost, which is obviously not suitable for practical applications.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • An MEC stochastic task migration method based on a Bayesian network
  • An MEC stochastic task migration method based on a Bayesian network
  • An MEC stochastic task migration method based on a Bayesian network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] Aiming at the existing MEC task migration problem, the present invention designs a MEC random task migration method based on Bayesian network. First, the application is transformed into a directed graph containing multiple subtasks, so as to represent the internal relationship between the subtasks of the application itself. On this basis, a random task migration algorithm based on Bayesian network is proposed, which uses Dependency estimates the energy consumption of each subtask to perform two migration decisions, and obtains the prior probability of each subtask to perform two migration decisions, and finally optimizes the energy consumption of mobile devices based on the prior probability Generate a set of scheduling policies.

[0031] figure 1 What is shown is a fine-grained subtask division diagram of a task (or application). In this paper, the application is divided into multiple independently executed subtasks, and represented by a directed graph G=(V,D). The n...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an MEC random task migration method based on a Bayesian network. The method comprises the following steps of converting an application into a directed graph containing a plurality of sub-tasks; using a probability calculation method of a sub-node in the Bayesian network to calculate a priori probability of a current sub-task migration decision; generating a set of scheduling strategies to minimize the energy consumption of mobile devices according to the probability; using the weak exhaustion algorithm to adjust the generated scheduling strategy so as to obtain the optimal computing task migration strategy. The technical proposal of the invention solves the problem of random task scheduling under the MEC scene.

Description

technical field [0001] The invention belongs to wireless network technology, in particular to a Bayesian network-based MEC random task migration method. Background technique [0002] In recent years, with the rapid development of the mobile Internet and the Internet of Things, higher requirements have been put forward for the delay and reliability of the network, and the multi-access edge computing (Multi-access Edge Computing, MEC) , can provide users with lower latency and more reliable network experience. In the MEC scenario, the distance between the user and the server is very close, and the data transmission rate will be very fast. When processing tasks, the powerful computing power of the server can be utilized and the resource consumption of mobile devices can be saved. Therefore, mobile devices are more inclined to migrate tasks to MEC servers to improve task execution performance and reduce task overhead on mobile devices. However, how the mobile device performs t...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/48
CPCG06F9/4875Y02D10/00
Inventor 霍如薛宁鄂新华刘江黄韬刘韵洁
Owner BEIJING UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap