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Energy-efficient fog computing migration method based on deep learning

A technology of deep learning and fog computing, applied in the direction of neural learning methods, biological neural network models, electrical components, etc., can solve problems such as network scenarios that cannot be applied to complex and dynamic changes, and achieve the goal of reducing task completion time and end-user energy consumption Effect

Active Publication Date: 2019-12-03
NANJING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

[0005] The second type of scheme only considers the minimization of energy consumption
[0007] However, the above-mentioned mainstream fog computing migration solutions cannot be applied to complex and dynamically changing network scenarios

Method used

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  • Energy-efficient fog computing migration method based on deep learning
  • Energy-efficient fog computing migration method based on deep learning
  • Energy-efficient fog computing migration method based on deep learning

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Embodiment Construction

[0046] This embodiment is as figure 1 As shown, the DL-FCOD algorithm designed by the present invention can automatically extract data features and generate adaptive migration decisions, thereby minimizing task completion time. Suppose a fog computing network consists of N end users and a fog server. In the present invention, the number of users is defined as N=5, and the computing capability of terminal user equipment C local =4Mb / s, fog server computing capacity C server =10Mb / s, channel attenuation coefficient g=1, channel transmission power N 0 for 10 -6 Watts, the power of an end-user device 4*10 -5 watt.

[0047] The completion time minimization model is as follows:

[0048] P1:

[0049] s.t.α n ={0,1},

[0050]

[0051]

[0052] The first constraint of the solution model in (1) represents the migration decision of user n’s real-time computing task, α n =1 indicates that the task is processed on the local device, α n= 0 indicates that the task is pro...

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Abstract

The invention discloses an energy efficient fog computing migration method based on deep learning. Firstly, a fog computing migration optimization problem of task completion time minimization is constructed, a fog computing migration decision algorithm based on deep learning is provided for solving the optimization problem, the algorithm has relatively fast convergence performance, and the task completion time in a complex network scene can be reduced to the greatest extent; secondly, in order to further optimize the energy consumption of fog computing migration, a terminal user energy consumption minimization fog computing migration optimization problem is constructed, an optimal transmission power distribution solving algorithm is provided for solving the optimization problem on the basis of an optimal migration decision solved by the migration decision algorithm, and the solving algorithm dynamically distributes transmission power, so that the optimal transmission power, namely theminimum energy consumption, is obtained; finally, the specific implementation of the method verifies the advantages of the fog computing migration method in reducing task completion time and user energy consumption.

Description

technical field [0001] The present invention relates to a fog computing migration method, in particular to an energy-efficient fog computing migration method based on deep learning. Background technique [0002] With the advent of the big data era, people's demand for computing resources and storage resources continues to rise, and traditional user equipment can no longer meet people's needs. The concept of cloud computing emerges as the times require, and the pay-as-you-go model it provides enables users to obtain required computing resources and storage resources at low prices. Users can transfer their computing tasks to remote cloud servers for processing. However, this long-distance transmission will cause a huge communication overhead and communication delay. The popularity of fog computing has made up for the above shortcomings to a certain extent. Fog nodes are closer to end users and have lower network delays. However, with the rise of resource-intensive tasks suc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/08G06N3/08
CPCH04L67/1004H04L67/12G06N3/08
Inventor 陈思光汤蓓郑忆敏王堃
Owner NANJING UNIV OF POSTS & TELECOMM
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