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A method and system for optimizing computing task network parameters combined with social perception

A technology for computing tasks and network architecture, applied in the field of MEC-D2D network applications, can solve the problems of energy waste, unstable performance, ignoring task offloading, energy consumption optimization and social networking, etc., to achieve the effect of reducing energy consumption

Active Publication Date: 2022-03-04
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the MEC-D2D network, the role of long-term task offloading energy optimization and social network is ignored
In the MEC-D2D network, because long-term energy consumption optimization and constraints are not considered, it will lead to serious energy waste and unstable performance in the long run. Connections can enhance users' trust in network connections and encourage users to use D2D

Method used

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  • A method and system for optimizing computing task network parameters combined with social perception
  • A method and system for optimizing computing task network parameters combined with social perception
  • A method and system for optimizing computing task network parameters combined with social perception

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Embodiment 1

[0040] Such as figure 1 As shown, this embodiment provides a method for optimizing network parameters of computing tasks combined with social awareness, including the following steps.

[0041] Step 101: Determine the MEC-D2D network architecture combined with social networks, data transmission model and consumption model corresponding to the research area.

[0042] The MEC-D2D network architecture is a model architecture established according to the service scenario corresponding to the research area; the MEC-D2D network architecture includes a centralized base station with a mobile edge computing server and multiple user equipments, the user equipment Including remote user equipment and near-end user equipment; in the MEC-D2D network architecture, each of the remote user equipment can only connect to one of the near-end user equipment, and each of the near-end user equipment can only connect to One remote user equipment; the data transmission model includes a computing task ...

Embodiment 2

[0076] Such as figure 2 As shown, the present embodiment provides a computing task network parameter optimization system combined with social awareness, including:

[0077] The research area model architecture determination module 201 is used to determine the MEC-D2D network architecture combined with social networks, data transmission model and consumption model corresponding to the research area; the MEC-D2D network architecture is established according to the service scene corresponding to the research area model architecture; the MEC-D2D network architecture includes a centralized base station with a mobile edge computing server and multiple user equipments, the user equipments include remote user equipments and near-end user equipments; each of the remote user equipments only One said near-end user equipment can be connected, and each said near-end user equipment can only be connected to one said remote user equipment; said data transmission model includes computing task...

Embodiment 3

[0087] This embodiment provides a computing task network parameter optimization method combined with social awareness, such as image 3 shown, including the following steps:

[0088] Step 1: Construct the MEC-D2D network architecture combined with social networks. This MEC-D2D network architecture is a model architecture established according to a service scenario, which is a scenario where an actual mobile edge computing server provides offloading services for remote user equipment. The MEC-D2D network architecture is divided into two network models, one is a device network model based on device connection relationships, and the other is a social relationship network model based on user social relationships.

[0089] Figure 4 For the MEC-D2D network architecture combined with social networks, such as Figure 4 As shown, it includes a centralized base station with a mobile edge computing server and multiple user equipments. The user equipment is divided into remote user eq...

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Abstract

The present invention discloses a computing task network parameter optimization method and system combined with social perception, and relates to the field of MEC-D2D network application technology, including determining the MEC-D2D network architecture, data transmission model, and consumption model combined with social networks corresponding to the research area and social trust matrix; according to the social trust matrix and the data in the consumption model, execute the consumption calculation function and the data transmission consumption calculation function to determine the maximum long-term system network utility function and constraint conditions corresponding to the research area, and use the Lyapunov optimization algorithm to maximize The system network utility function is decoupled to obtain three deterministic optimization objective functions; according to the total amount of computing task data and optimization algorithm corresponding to the research area, the above three deterministic optimization objective functions are solved to obtain the optimal network parameters. The application of the above network parameters in the present invention can reduce the energy consumption of the device execution process and the device transmission process in the MEC-D2D network for a long time.

Description

technical field [0001] The present invention relates to the field of MEC-D2D network application technology, in particular to a method and system for optimizing network parameters of computing tasks combined with social perception. Background technique [0002] With the popularization of mobile devices and the development of wireless communication such as 5G, many computing-intensive applications with low latency requirements have emerged, such as online immersive games, augmented reality, and video streaming analysis, etc. However, traditional cloud computing cannot meet the low-latency requirements of these applications, so researchers have proposed a new computing model called mobile edge computing (MEC, mobile edge computing). This computing mode transfers the computing task load from the remote cloud to the edge nodes of the core network (such as wireless access points, base stations, etc.) The user's quality of service (QoS, qualityofservice) requirement. However, wi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W4/70H04W24/02H04W24/06G06Q50/00
CPCH04W4/70H04W24/02H04W24/06G06Q50/01Y02D30/70
Inventor 许晨隆豪绳韵郑光远
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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